awesome-streaming-graphs

Must-read papers on streaming graphs

Content

1. Keynotes
2. Surveys
3. Systems
4. Streaming Graph Partitioning
5. Summarization & Sampling
5.1 Graph Stream Summarization	5.2 Graph Sampling
6. Continuous Queries
6.1 Subgraph Matching	6.2 Regular Path Query
6.3 Reachability	6.4 Shortest Path
6.5 Connectivity
7. Subgraph Counting & Mining
7.1 Triangle Count	7.2 Butterfly Count
7.3 Subgraph Counting	7.4 Frequent Subgraph Mining
7.5 Cycle, Clique & Biclique
8. Cohesive Subgraphs & Communities
8.1 Core & Tip Decomposition	8.2 Dense Subgraph
8.3 Community Detection	8.4 Community Search
8.5 Graph Clustering
9. Graph Analytics
10. Anomaly & Event Detection
10.1 Anomaly Detection	10.2 Burst & Event Detection
10.3 Concept Drift
11. Learning on Streaming Graphs
11.1 Representation Learning	11.2 Node & Graph Classification
11.3 Link Prediction & Recommendation	11.4 Generation & Forecasting

Tags

Each paper entry ends with tags:

• Result: Exact means the method returns the exact answer to the problem as defined; randomized methods that are exact with high probability, such as connectivity sketches, also count as Exact. Approx means it returns an estimate or an approximation (sketching, sampling, heuristics, learned estimators). Neural-network detectors in Section 10.1, and papers in Section 11, carry no result tag.
• Data model: Insert-only means edges (or graphs) only arrive. Fully dynamic means edges can be both inserted and deleted; edge-weight increases and decreases, as in dynamic road networks, also count. Sliding window means only edges within a recent time or count window are valid, and older edges expire. Snapshots means the input is a sequence of graph snapshots.

Keynotes

1. Streaming Graph Processing and Analytics. [slides] [DEBS'20 video] [PKUMOD'20 video]

   M. Tamer Özsu.

2. Graph Processing: A Panoramic View and Some Open Problems. [slides] [VLDB'19 video] [PKUMOD'20 video]

   M. Tamer Özsu.

3. An Introduction to Graph Analytics Platform - Very Short Version. [slides]

   M. Tamer Özsu.

Surveys

1. Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems. IEEE Trans. Parallel Distributed Syst. 34(6): 1860-1876 (2023) [paper]

   Maciej Besta, Marc Fischer, Vasiliki Kalavri, Michael Kapralov, Torsten Hoefler.

2. 图数据流的模型、算法和系统[J]. 大数据, 2018, 4(4): 44-55. [paper]

   李友焕, 邹磊.

3. Graph stream algorithms: a survey. SIGMOD Rec. 43(1): 9-20 (2014) [paper]

   Andrew McGregor.

Systems

1. A Comprehensive Survey on Dynamic Graph Processing: Storage and Analytics. IEEE Trans. Knowl. Data Eng. 38(5): 2488-2507 (2026) [paper] Survey

   Yuan Zhang, Huawei Cao, Yue Shen, Sibo Li, Yang Feng, Chunxiang Wang, Xuejun An, Xiaochun Ye.

2. CEGraph: Cache-Efficient Management for Streaming Graph Processing. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 45(1): 108-119 (2026) [paper] Fully dynamic

   Fubing Mao, Zihan Xie, Longyu Nie, Yu Zhang, Haikun Liu, Xiaofei Liao, Hai Jin, Wei Zhang, Yapu Guo, Jingkang Liu.

3. DCSR: A Fast Data Structure with Leaf-Oriented Locks for Streaming Graph Processing. EDBT 2026: 359-371 [paper] Fully dynamic

   Yue Shen, Jie Zhang, Huawei Cao, Yuan Zhang, Xuejun An.

4. SVSIG: Incremental Streaming Graph Processing with Source Vertex Suppression. ICS 2026: 539-550 [paper] Fully dynamic

   Jianqiang Huang, Xiao Yan, Dapeng Fu, Haodong Bian, Tengfei Cao, Zhixiong Li.

5. WSGraph: A Framework for Tackling Redundant and Irregular Data Access in Streaming Graph Processing. ACM Trans. Archit. Code Optim. 23(2): 54:1-54:25 (2026) [paper] Fully dynamic

   Xuanyi Li, Chen Li, Zhengyi Dai, Xiaowen Chen, Jianzhuang Lu, Yang Guo.

6. CISGraph: A Contribution-Driven Accelerator for Pairwise Streaming Graph Analytics. DATE 2025: 1-7 [paper] Fully dynamic

   Songyu Feng, Mo Zou, Tian Zhi, Zidong Du.

7. Efficient Graph Data Access for Out-of-Memory GPU Streaming Graph Processing. Proc. VLDB Endow. 18(11): 3854-3867 (2025) [paper] [code] Fully dynamic

   Qiange Wang, Yongze Yan, Hongshi Tan, Cheng Chen, Cheng Zhao, Jiaming Tian, Jiaxin Jiang, Xiaoliang Cong, Yanfeng Zhang, Ge Yu, Weng-Fai Wong, Bingsheng He.

8. AeonG: An Efficient Built-in Temporal Support in Graph Databases. Proc. VLDB Endow. 17(6): 1515-1527 (2024) [paper] [code] Fully dynamic

   Jiamin Hou, Zhanhao Zhao, Zhouyu Wang, Wei Lu, Guodong Jin, Dong Wen, Xiaoyong Du.

9. LSGraph: A Locality-centric High-performance Streaming Graph Engine. EuroSys 2024: 33-49 [paper] Fully dynamic

   Hao Qi, Yiyang Wu, Ligang He, Yu Zhang, Kang Luo, Minzhi Cai, Hai Jin, Zhan Zhang, Jin Zhao.

10. PDG: A Prefetcher for Dynamic Graph Updating. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 43(4): 1246-1259 (2024) [paper] Fully dynamic

    Xinmiao Zhang, Cheng Liu, Jiacheng Ni, Yuanqing Cheng, Lei Zhang, Huawei Li, Xiaowei Li.

11. PMGraph: Accelerating Concurrent Graph Queries over Streaming Graphs. ACM Trans. Archit. Code Optim. 21(4): 88:1-88:25 (2024) [paper] Fully dynamic

    Fubing Mao, Xu Liu, Yu Zhang, Haikun Liu, Xiaofei Liao, Hai Jin, Wei Zhang, Jian Zhou, Yufei Wu, Longyu Nie, Yapu Guo, Zihan Jiang, Jingkang Liu.

12. TARIS: Scalable Incremental Processing of Time-Respecting Algorithms on Streaming Graphs. IEEE Trans. Parallel Distributed Syst. 35(12): 2527-2544 (2024) [paper] Fully dynamic

    Ruchi Bhoot, Suved Sanjay Ghanmode, Yogesh Simmhan.

13. ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy. DAC 2023: 1-6 [paper] Fully dynamic

    Zihan Jiang, Fubing Mao, Yapu Guo, Xu Liu, Haikun Liu, Xiaofei Liao, Hai Jin, Wei Zhang.

14. GeaFlow: A Graph Extended and Accelerated Dataflow System. Proc. ACM Manag. Data 1(2): 191:1-191:27 (2023) [paper] Fully dynamic

    Zhenxuan Pan, Tao Wu, Qingwen Zhao, Qiang Zhou, Zhiwei Peng, Jiefeng Li, Qi Zhang, Guanyu Feng, Xiaowei Zhu.

15. GraphFly: Efficient Asynchronous Streaming Graphs Processing via Dependency-Flow. SC 2022: 45:1-45:14 [paper] Fully dynamic

    Dan Chen, Chuangyi Gui, Yi Zhang, Hai Jin, Long Zheng, Yu Huang, Xiaofei Liao.

16. Optimizing Differentially-Maintained Recursive Queries on Dynamic Graphs. Proc. VLDB Endow. 15(11): 3186-3198 (2022) [paper] [code] Fully dynamic

    Khaled Ammar, Siddhartha Sahu, Semih Salihoglu, M. Tamer Özsu.

17. TDGraph: a topology-driven accelerator for high-performance streaming graph processing. ISCA 2022: 116-129 [paper] Fully dynamic

    Jin Zhao, Yun Yang, Yu Zhang, Xiaofei Liao, Lin Gu, Ligang He, Bingsheng He, Hai Jin, Haikun Liu, Xinyu Jiang, Hui Yu.

18. Automating Incremental Graph Processing with Flexible Memoization. Proc. VLDB Endow. 14(9): 1613-1625 (2021) [paper] Fully dynamic

    Shufeng Gong, Chao Tian, Qiang Yin, Wenyuan Yu, Yanfeng Zhang, Liang Geng, Song Yu, Ge Yu, Jingren Zhou.

19. Controlling Memory Footprint of Stateful Streaming Graph Processing. USENIX ATC 2021: 269-283 [paper] [slides] [video] Fully dynamic

    Pourya Vaziri, Keval Vora.

20. DZiG: sparsity-aware incremental processing of streaming graphs. EuroSys 2021: 83-98 [paper] Fully dynamic

    Mugilan Mariappan, Joanna Che, Keval Vora.

21. Exploiting Buffered Updates for Fast Streaming Graph Analysis. IEEE Trans. Computers 70(2): 255-269 (2021) [paper] Fully dynamic

    Feng Sheng, Qiang Cao, Jie Yao.

22. RisGraph: A Real-Time Streaming System for Evolving Graphs to Support Sub-millisecond Per-update Analysis at Millions Ops/s. SIGMOD Conference 2021: 513-527 [paper] Fully dynamic

    Guanyu Feng, Zixuan Ma, Daixuan Li, Shengqi Chen, Xiaowei Zhu, Wentao Han, Wenguang Chen.

23. Tripoline: generalized incremental graph processing via graph triangle inequality. EuroSys 2021: 17-32 [paper] Insert-only

    Xiaolin Jiang, Chengshuo Xu, Xizhe Yin, Zhijia Zhao, Rajiv Gupta.

24. Dolha - an efficient and exact data structure for streaming graphs. World Wide Web 23(2): 873-903 (2020) [paper] Fully dynamic Sliding window

    Fan Zhang, Lei Zou, Li Zeng, Xiangyang Gou.

25. GraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs. EuroSys 2019: 25:1-25:16 [paper] Fully dynamic

    Mugilan Mariappan, Keval Vora.

26. GraphOne: A Data Store for Real-time Analytics on Evolving Graphs. FAST 2019: 249-263 [paper] [slides] [video] Fully dynamic

    Pradeep Kumar, H. Howie Huang.

27. KickStarter: Fast and Accurate Computations on Streaming Graphs via Trimmed Approximations. ASPLOS 2017: 237-251 [paper] Fully dynamic

    Keval Vora, Rajiv Gupta, Guoqing Xu.

Streaming Graph Partitioning

1. FREIGHT: Fast Streaming Hypergraph Partitioning. Algorithmica 87(3): 405-428 (2025) [paper]

   Kamal Eyubov, Marcelo Fonseca Faraj, Christian Schulz.

2. Triparts: Scalable Streaming Graph Partitioning to Enhance Community Structure. Proc. VLDB Endow. 18(9): 2992-3006 (2025) [paper] [code]

   Ruchi Bhoot, Tuhin Khare, Manoj Agarwal, Siddharth Jaiswal, Yogesh Simmhan.

3. CUTTANA: Scalable Graph Partitioning for Faster Distributed Graph Databases and Analytics. Proc. VLDB Endow. 18(1): 14-27 (2024) [paper] [code]

   Milad Rezaei Hajidehi, Sraavan Sridhar, Margo I. Seltzer.

4. Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning. Proc. ACM Manag. Data 2(3): 162 (2024) [paper]

   Zezhong Ding, Yongan Xiang, Shangyou Wang, Xike Xie, S. Kevin Zhou.

5. A Mixed-State Streaming Edge Partitioning based on Combinatorial Design. ICDM 2023: 868-877 [paper]

   Zhenyu Zhang, Wenwen Qu, Weixi Zhang, Junlin Shang, Xiaoling Wang.

6. Attribute-driven streaming edge partitioning with reconciliations for distributed graph neural network training. Neural Networks 165: 987-998 (2023) [paper]

   Zongshen Mu, Siliang Tang, Yueting Zhuang, Dianhai Yu.

7. Lightweight Streaming Graph Partitioning by Fully Utilizing Knowledge from Local View. ICDCS 2023: 614-625 [paper]

   Zhigang Wang, Zichao Yang, Ning Wang, Yujie Du, Jie Nie, Zhiqiang Wei, Yu Gu, Ge Yu.

8. Out-of-Core Edge Partitioning at Linear Run-Time. ICDE 2022: 2629-2642 [paper] [code]

   Ruben Mayer, Kamil Orujzade, Hans-Arno Jacobsen.

9. Recursive Multi-Section on the Fly: Shared-Memory Streaming Algorithms for Hierarchical Graph Partitioning and Process Mapping. CLUSTER 2022: 473-483 [paper]

   Marcelo Fonseca Faraj, Christian Schulz.

10. Experimental Analysis of Streaming Algorithms for Graph Partitioning. SIGMOD Conference 2019: 1375-1392 [paper] [code] Benchmark

    Anil Pacaci, M. Tamer Özsu.

11. Quasi-Streaming Graph Partitioning: A Game Theoretical Approach. IEEE Trans. Parallel Distributed Syst. 30(7): 1643-1656 (2019) [paper]

    Qiang-Sheng Hua, Yangyang Li, Dongxiao Yu, Hai Jin.

12. Streaming Graph Partitioning: An Experimental Study. Proc. VLDB Endow. 11(11): 1590-1603 (2018) [paper] Benchmark

    Zainab Abbas, Vasiliki Kalavri, Paris Carbone, Vladimir Vlassov.

13. Modeling, analysis, and experimental comparison of streaming graph-partitioning policies. J. Parallel Distributed Comput. 108: 106-121 (2017) [paper] Benchmark

    Yong Guo, Sungpack Hong, Hassan Chafi, Alexandru Iosup, Dick Epema.

14. HDRF: Stream-Based Partitioning for Power-Law Graphs. CIKM 2015: 243-252 [paper]

    Fabio Petroni, Leonardo Querzoni, Khuzaima Daudjee, Shahin Kamali, Giorgio Iacoboni.

15. Distributed Power-law Graph Computing: Theoretical and Empirical Analysis. NIPS 2014: 1673-1681 [paper]

    Cong Xie, Ling Yan, Wu-Jun Li, Zhihua Zhang.

16. FENNEL: streaming graph partitioning for massive scale graphs. WSDM 2014: 333-342 [paper]

    Charalampos E. Tsourakakis, Christos Gkantsidis, Bozidar Radunovic, Milan Vojnovic.

17. GraphBuilder: scalable graph ETL framework. GRADES 2013: 4 [paper]

    Nilesh Jain, Guangdeng Liao, Theodore L. Willke.

18. PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs. OSDI 2012: 17-30 [paper]

    Joseph E. Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, Carlos Guestrin.

19. Streaming graph partitioning for large distributed graphs. KDD 2012: 1222-1230 [paper]

    Isabelle Stanton, Gabriel Kliot.

Summarization & Sampling

Graph Stream Summarization

1. Crane: An Accurate and Scalable Neural Sketch for Graph Stream Summarization. Proc. ACM Manag. Data 4(6): 330 (2026) [paper] Approx Insert-only

   Boyan Wang, Zhuochen Fan, Dayu Wang, Fangcheng Fu, Zeyu Luan, Lei Zou, Qing Li, Tong Yang.

2. GeminiSketch: An Accurate and Efficient Sketch for Summarizing Temporal Graph Streams with Rolling-Out Elimination. ICDE 2026: 30-43 [paper] Approx Sliding window

   Xuyang Jing, Chenhao Zhang, Zheng Yan, Qingze Jiang, Witold Pedrycz, Mingjun Wang, Cong Wang.

3. GRIT: An Accurate and Efficient Graph Stream Summarization for Temporal Query. CIKM 2025: 888-897 [paper] Approx Insert-only

   Jingxian Hu, Guozhang Sun, Xin Wang, Yuhai Zhao, Yuan Li, Xingwei Wang.

4. HIGGS: HIerarchy-Guided Graph Stream Summarization. ICDE 2025: 1470-1482 [paper] Approx Fully dynamic

   Xuan Zhao, Xike Xie, Christian S. Jensen.

5. HourglassSketch: An Efficient and Scalable Framework for Graph Stream Summarization. ICDE 2025: 114-127 [paper] [code] Approx Insert-only

   Jiarui Guo, Boxuan Chen, Kaicheng Yang, Tong Yang, Zirui Liu, Qiuheng Yin, Sha Wang, Yuhan Wu, Xiaolin Wang, Bin Cui, Tao Li, Xi Peng, Renhai Chen, Gong Zhang.

6. LSketch: A label-enabled graph stream sketch toward time-sensitive queries. Inf. Sci. 691: 121624 (2025) [paper] Approx Sliding window

   Yiling Zeng, Chuanfeng Jian, Chunyao Song, Tingjian Ge, Yuhan Li, Yuqing Zhou.

7. Sliding-ITeM: An Adaptive-Size Graph Stream Summarization Structure Based on Sliding Windows. Data Sci. Eng. 10(3): 428-454 (2025) [paper] Approx Sliding window

   Yacheng Wang, Hanhua Chen, Hao Chen, Hai Jin.

8. Toward accurate weight-based measurement and periodic edge measurement in graph stream. World Wide Web 28(6): 63 (2025) [paper] Approx Insert-only

   Zhuo Li, YuXuan Zhao, Jindian Liu, Yu Zhang, Kaihua Liu.

9. Conditional heavy hitter monitoring and application of heterogeneous graph streams based on sketches. Inf. Process. Manag. 61(4): 103762 (2024) [paper] [code] Approx Insert-only

   Chuanfeng Jian, Yiling Zeng, Chunyao Song, Ying Zhang.

10. Learning to Sketch: A Neural Approach to Item Frequency Estimation in Streaming Data. IEEE Trans. Pattern Anal. Mach. Intell. 46(11): 7136-7153 (2024) [paper] [code] Approx Insert-only

    Yukun Cao, Yuan Feng, Hairu Wang, Xike Xie, S. Kevin Zhou.

11. Mayfly: a Neural Data Structure for Graph Stream Summarization. ICLR 2024 [paper] [code] Approx Insert-only

    Yuan Feng, Yukun Cao, Hairu Wang, Xike Xie, S. Kevin Zhou.

12. SIM: A fast real-time graph stream summarization with improved memory efficiency and accuracy. Comput. Networks 248: 110502 (2024) [paper] Approx Insert-only

    Zhuo Li, Shuaijun Liu, Jindian Liu, Yu Zhang, Teng Liang, Kaihua Liu.

13. Auxo: A Scalable and Efficient Graph Stream Summarization Structure. Proc. VLDB Endow. 16(6): 1386-1398 (2023) [paper] [code] Approx Insert-only

    Zhiguo Jiang, Hanhua Chen, Hai Jin.

14. Balancing Summarization and Change Detection in Graph Streams. ICDM 2023: 1025-1030 [paper] [code] Approx Snapshots

    Shintaro Fukushima, Kenji Yamanishi.

15. Graph Stream Sketch: Summarizing Graph Streams With High Speed and Accuracy. IEEE Trans. Knowl. Data Eng. 35(6): 5901-5914 (2023) [paper] Approx Fully dynamic

    Xiangyang Gou, Lei Zou, Chenxingyu Zhao, Tong Yang.

16. Learning-Based Dichotomy Graph Sketch for Summarizing Graph Streams with High Accuracy. KSEM 2023: 47-59 [paper] Approx Insert-only

    Ding Li, Wenzhong Li, Yizhou Chen, Xu Zhong, Mingkai Lin, Sanglu Lu.

17. Persistent graph stream summarization for real-time graph analytics. World Wide Web 26(5): 2647-2667 (2023) [paper] Approx Insert-only

    Yan Jia, Zhaoquan Gu, Zhihao Jiang, Cuiyun Gao, Jianye Yang.

18. A parameter-free approach to lossless summarization of fully dynamic graphs. Inf. Sci. 589: 376-394 (2022) [paper] Exact Fully dynamic

    Ziyi Ma, Yuling Liu, Zhibang Yang, Jianye Yang, Kenli Li.

19. Dynamic Graph Summarization: Optimal and Scalable. IEEE BigData 2022: 545-554 [paper] Exact Fully dynamic

    Mahdi Hajiabadi, Venkatesh Srinivasan, Alex Thomo.

20. Horae: A Graph Stream Summarization Structure for Efficient Temporal Range Query. ICDE 2022: 2792-2804 [paper] Approx Insert-only

    Ming Chen, Renxiang Zhou, Hanhua Chen, Jiang Xiao, Hai Jin, Bo Li.

21. Scube: Efficient Summarization for Skewed Graph Streams. ICDCS 2022: 100-110 [paper] Approx Insert-only

    Ming Chen, Renxiang Zhou, Hanhua Chen, Hai Jin.

22. A Parameter-Free Approach for Lossless Streaming Graph Summarization. DASFAA (1) 2021: 385-393 [paper] Exact Insert-only

    Ziyi Ma, Jianye Yang, Kenli Li, Yuling Liu, Xu Zhou, Yikun Hu.

23. DMatrix: Toward fast and accurate queries in graph stream. Comput. Networks 198: 108403 (2021) [paper] [code] Approx Insert-only

    Changsheng Hou, Bingnan Hou, Tongqing Zhou, Zhiping Cai.

24. GS4: Graph stream summarization based on both the structure and semantics. J. Supercomput. 77(3): 2713-2733 (2021) [paper] Approx Sliding window

    Nosratali Ashrafi-Payaman, Mohammad Reza Kangavari, Saeid Hosseini, Amir Mohammad Fander.

25. Learning-Based Dynamic Graph Stream Sketch. PAKDD 2021: 383-394 [paper] Approx Insert-only

    Ding Li, Wenzhong Li, Yizhou Chen, Mingkai Lin, Sanglu Lu.

26. Incremental Lossless Graph Summarization. KDD 2020: 317-327 [paper] [slides] [video] Exact Fully dynamic

    Jihoon Ko, Yunbum Kook, Kijung Shin.

27. Fast and Accurate Graph Stream Summarization. ICDE 2019: 1118-1129 [paper] [code] Approx Insert-only

    Xiangyang Gou, Lei Zou, Chenxingyu Zhao, Tong Yang.

28. Labeled graph sketches: Keeping up with real-time graph streams. Inf. Sci. 503: 469-492 (2019) [paper] Approx Fully dynamic Sliding window

    Chunyao Song, Tingjian Ge, Yao Ge, Haowen Zhang, Xiaojie Yuan.

29. SBG-sketch: a self-balanced sketch for labeled-graph stream summarization. SSDBM 2018: 3:1-3:12 [paper] Approx Insert-only

    Mohamed S. Hassan, Bruno Ribeiro, Walid G. Aref.

30. Graph Stream Summarization: From Big Bang to Big Crunch. SIGMOD Conference 2016: 1481-1496 [paper] [slides] Approx Fully dynamic

    Nan Tang, Qing Chen, Prasenjit Mitra.

31. Query-friendly compression of graph streams. ASONAM 2016: 130-137 [paper] Approx Insert-only

    Arijit Khan, Charu C. Aggarwal.

32. gSketch: On Query Estimation in Graph Streams. Proc. VLDB Endow. 5(3): 193-204 (2011) [paper] Approx Insert-only

    Peixiang Zhao, Charu C. Aggarwal, Min Wang.

Graph Sampling

1. Computing Graph Descriptors on Edge Streams. ACM Trans. Knowl. Discov. Data 17(8): 112:1-112:25 (2023) [paper] Approx Insert-only

   Zohair Raza Hassan, Sarwan Ali, Imdadullah Khan, Mudassir Shabbir, Waseem Abbas.

2. Adaptive Shrinkage Estimation for Streaming Graphs. NeurIPS 2020 [paper] Approx Insert-only

   Nesreen K. Ahmed, Nick Duffield.

3. Cluster-preserving sampling from fully-dynamic streaming graphs. Inf. Sci. 482: 279-300 (2019) [paper] Approx Fully dynamic

   Jianpeng Zhang, Kaijie Zhu, Yulong Pei, George Fletcher, Mykola Pechenizkiy.

4. ComPAS: Community Preserving Sampling for Streaming Graphs. AAMAS 2018: 184-192 [paper] Approx Insert-only

   Sandipan Sikdar, Tanmoy Chakraborty, Soumya Sarkar, Niloy Ganguly, Animesh Mukherjee.

5. On sampling from massive graph streams. Proc. VLDB Endow. 10(11): 1430-1441 (2017) [paper] Approx Insert-only

   Nesreen K. Ahmed, Nick Duffield, Theodore L. Willke, Ryan A. Rossi.

6. Graph sample and hold: a framework for big-graph analytics. KDD 2014: 1446-1455 [paper] Approx Insert-only

   Nesreen K. Ahmed, Nick Duffield, Jennifer Neville, Ramana Kompella.

7. Network Sampling: From Static to Streaming Graphs. ACM Trans. Knowl. Discov. Data 8(2): 7:1-7:56 (2014) [paper] Approx Insert-only

   Nesreen K. Ahmed, Jennifer Neville, Ramana Kompella.

Continuous Queries

Subgraph Matching

1. An Extensive Experimental Study of Indexes in Continuous Subgraph Matching. Proc. ACM Manag. Data 4(1): 9:1-9:26 (2026) [paper] Exact Fully dynamic Benchmark

   Xiangyang Gou, Lei Zou, Jeffrey Xu Yu, Wenjie Zhang.

2. Efficient Multi-Query Optimization for Continuous Subgraph Matching Over Streaming Graphs. IEEE Trans. Knowl. Data Eng. 38(11): 7445-7460 (2026) [paper] [code] Exact Fully dynamic

   Ziyi Ma, Xinming Ma, Jianye Yang, Liang Yang, Lihua Yin, Xuemin Lin.

3. Privacy-Preserving Analytics on Outsourced Streaming Graphs: The Case of Pattern Detection. IEEE Trans. Knowl. Data Eng. 38(5): 3037-3051 (2026) [paper] Exact Fully dynamic Sliding window

   Songlei Wang, Yifeng Zheng, Xiaohua Jia.

4. Continuous Subgraph Matching via Cost-Model-based Dynamic Vertex Dominance Embeddings. Proc. ACM Manag. Data 3(6): 309:1-309:27 (2025) [paper] Exact Fully dynamic

   Yutong Ye, Xiang Lian, Nan Zhang, Mingsong Chen.

5. CSM-TopK: Continuous Subgraph Matching with TopK Density Constraints. ICDE 2024: 3084-3097 [paper] [code] Exact Fully dynamic

   Chuchu Gao, Youhuan Li, Zhibang Yang, Xu Zhou.

6. Efficient Multi-Query Oriented Continuous Subgraph Matching. ICDE 2024: 3230-3243 [paper] [code] Exact Fully dynamic

   Ziyi Ma, Jianye Yang, Xu Zhou, Guoqing Xiao, Jianhua Wang, Liang Yang, Kenli Li, Xuemin Lin.

7. GCSM: GPU-Accelerated Continuous Subgraph Matching for Large Graphs. IPDPS 2024: 1046-1057 [paper] Exact Fully dynamic

   Yihua Wei, Peng Jiang.

8. GraphGuard: Private Time-Constrained Pattern Detection Over Streaming Graphs in the Cloud. USENIX Security Symposium 2024 [paper] [slides] [video] Exact Sliding window

   Songlei Wang, Yifeng Zheng, Xiaohua Jia.

9. In-depth Analysis of Continuous Subgraph Matching in a Common Delta Query Compilation Framework. Proc. ACM Manag. Data 2(3): 147:1-147:27 (2024) [paper] Exact Fully dynamic Benchmark

   Yukyoung Lee, Kyoungmin Kim, Wonseok Lee, Wook-Shin Han.

10. NewSP: A New Search Process for Continuous Subgraph Matching over Dynamic Graphs. ICDE 2024: 3324-3337 [paper] Exact Fully dynamic

    Ziming Li, Youhuan Li, Xinhuan Chen, Lei Zou, Yang Li, Xiaofeng Yang, Hongbo Jiang.

11. TC-Match: Fast Time-constrained Continuous Subgraph Matching. Proc. VLDB Endow. 17(11): 2791-2804 (2024) [paper] [code] Exact Sliding window

    Jianye Yang, Sheng Fang, Zhaoquan Gu, Ziyi Ma, Xuemin Lin, Zhihong Tian.

12. Time-Constrained Continuous Subgraph Matching Using Temporal Information for Filtering and Backtracking. ICDE 2024: 3257-3269 [paper] Exact Sliding window

    Seunghwan Min, Jihoon Jang, Kunsoo Park, Dora Giammarresi, Giuseppe F. Italiano, Wook-Shin Han.

13. A survey of continuous subgraph matching for dynamic graphs. Knowl. Inf. Syst. 65(3): 945-989 (2023) [paper] Survey

    Xi Wang, Qianzhen Zhang, Deke Guo, Xiang Zhao.

14. Fast Continuous Subgraph Matching over Streaming Graphs via Backtracking Reduction. Proc. ACM Manag. Data 1(1): 15:1-15:26 (2023) [paper] [code] [video] Exact Fully dynamic

    Rongjian Yang, Zhijie Zhang, Weiguo Zheng, Jeffrey Xu Yu.

15. An In-Depth Study of Continuous Subgraph Matching. Proc. VLDB Endow. 15(7): 1403-1416 (2022) [paper] [code] Exact Fully dynamic Benchmark

    Xibo Sun, Shixuan Sun, Qiong Luo, Bingsheng He.

16. Mnemonic: A Parallel Subgraph Matching System for Streaming Graphs. IPDPS 2022: 313-323 [paper] Exact Fully dynamic Sliding window

    Bibek Bhattarai, H. Howie Huang.

17. RapidFlow: An Efficient Approach to Continuous Subgraph Matching. Proc. VLDB Endow. 15(11): 2415-2427 (2022) [paper] [code] Exact Fully dynamic

    Shixuan Sun, Xibo Sun, Bingsheng He, Qiong Luo.

18. Space-Efficient Subgraph Search Over Streaming Graph With Timing Order Constraint. IEEE Trans. Knowl. Data Eng. 34(9): 4453-4467 (2022) [paper] Exact Sliding window

    Youhuan Li, Lei Zou, M. Tamer Özsu, Dongyan Zhao.

19. Continuous matching of evolving patterns over dynamic graph data. World Wide Web 24(3): 721-745 (2021) [paper] Exact Fully dynamic

    Qianzhen Zhang, Deke Guo, Xiang Zhao, Xi Wang.

20. Symmetric Continuous Subgraph Matching with Bidirectional Dynamic Programming. Proc. VLDB Endow. 14(8): 1298-1310 (2021) [paper] [code] Exact Fully dynamic

    Seunghwan Min, Sung Gwan Park, Kunsoo Park, Dora Giammarresi, Giuseppe F. Italiano, Wook-Shin Han.

21. Efficient Continuous Multi-Query Processing over Graph Streams. EDBT 2020: 13-24 [paper] Exact Fully dynamic

    Lefteris Zervakis, Vinay Setty, Christos Tryfonopoulos, Katja Hose.

22. General dynamic Yannakakis: conjunctive queries with theta joins under updates. VLDB J. 29(2-3): 619-653 (2020) [paper] Exact Fully dynamic

    Muhammad Idris, Martín Ugarte, Stijn Vansummeren, Hannes Voigt, Wolfgang Lehner.

23. Time Constrained Continuous Subgraph Search Over Streaming Graphs. ICDE 2019: 1082-1093 [paper] [code] Exact Sliding window

    Youhuan Li, Lei Zou, M. Tamer Özsu, Dongyan Zhao.

24. Distributed evaluation of subgraph queries using worst-case optimal low-memory dataflows. Proc. VLDB Endow. 11(6): 691-704 (2018) [paper] Exact Fully dynamic

    Khaled Ammar, Frank McSherry, Semih Salihoglu, Manas Joglekar.

25. TurboFlux: A Fast Continuous Subgraph Matching System for Streaming Graph Data. SIGMOD Conference 2018: 411-426 [paper] Exact Fully dynamic

    Kyoungmin Kim, In Seo, Wook-Shin Han, Jeong-Hoon Lee, Sungpack Hong, Hassan Chafi, Hyungyu Shin, Geonhwa Jeong.

26. Graphflow: An active graph database. SIGMOD Conference 2017: 1695-1698 [paper] Exact Fully dynamic

    Chathura Kankanamge, Siddhartha Sahu, Amine Mhedhbi, Jeremy Chen, Semih Salihoglu.

27. The Dynamic Yannakakis Algorithm: Compact and Efficient Query Processing Under Updates. SIGMOD Conference 2017: 1259-1274 [paper] Exact Fully dynamic

    Muhammad Idris, Martín Ugarte, Stijn Vansummeren.

28. A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs. EDBT 2015: 157-168 [paper] [slides] Exact Sliding window

    Sutanay Choudhury, Lawrence B. Holder, George Chin Jr., Khushbu Agarwal, John Feo.

29. Event pattern matching over graph streams. Proc. VLDB Endow. 8(4): 413-424 (2014) [paper] Exact Sliding window

    Chunyao Song, Tingjian Ge, Cindy Chen, Jie Wang.

30. Incremental graph pattern matching. ACM Trans. Database Syst. 38(3): 18 (2013) [paper] Exact Fully dynamic

    Wenfei Fan, Xin Wang, Yinghui Wu.

31. Incremental graph pattern matching. SIGMOD Conference 2011: 925-936 [paper] Exact Fully dynamic

    Wenfei Fan, Jianzhong Li, Jizhou Luo, Zijing Tan, Xin Wang, Yinghui Wu.

32. Continuous Subgraph Pattern Search over Certain and Uncertain Graph Streams. IEEE Trans. Knowl. Data Eng. 22(8): 1093-1109 (2010) [paper] Approx Fully dynamic

    Lei Chen, Changliang Wang.

33. Continuous Subgraph Pattern Search over Graph Streams. ICDE 2009: 393-404 [paper] Approx Fully dynamic

    Changliang Wang, Lei Chen.

Regular Path Query

1. DRPQ: Distributed Evaluation of Regular Path Queries On Streaming Graphs. Proc. ACM Manag. Data 3(6): 317:1-317:27 (2025) [paper] Exact Fully dynamic Sliding window

   Siyuan Zhang, Kai Zhang, Zhenying He, Yinan Jing, Zhigang Zhao, X. Sean Wang.

2. LM-SRPQ: Efficiently Answering Regular Path Query in Streaming Graphs. Proc. VLDB Endow. 17(5): 1047-1059 (2024) [paper] [code] Exact Sliding window

   Xiangyang Gou, Xinyi Ye, Lei Zou, Jeffrey Xu Yu.

3. MWP: Multi-Window Parallel Evaluation of Regular Path Queries on Streaming Graphs. Proc. ACM Manag. Data 2(1): 5:1-5:26 (2024) [paper] Exact Fully dynamic Sliding window

   Siyuan Zhang, Zhenying He, Yinan Jing, Kai Zhang, X. Sean Wang.

4. Seraph: Continuous Queries on Property Graph Streams. EDBT 2024: 234-247 [paper] Exact Sliding window

   Christopher Rost, Riccardo Tommasini, Angela Bonifati, Emanuele Della Valle, Erhard Rahm, Keith W. Hare, Stefan Plantikow, Petra Selmer, Hannes Voigt.

5. Evaluating complex queries on streaming graphs. ICDE 2022: 272-285 [paper] [code] [video] Exact Sliding window

   Anil Pacaci, Angela Bonifati, M. Tamer Özsu.

6. Regular Path Query Evaluation on Streaming Graphs. SIGMOD Conference 2020: 1415-1430 [paper] [video] Exact Fully dynamic Sliding window

   Anil Pacaci, Angela Bonifati, M. Tamer Özsu.

Reachability

1. Efficient algorithms for reachability and path queries on temporal bipartite graphs. VLDB J. 33(5): 1399-1426 (2024) [paper] Exact Insert-only

   Kai Wang, Minghao Cai, Xiaoshuang Chen, Xuemin Lin, Wenjie Zhang, Lu Qin, Ying Zhang.

2. Reachability Queries on Dynamic Temporal Bipartite Graphs. SIGSPATIAL/GIS 2023: 97:1-97:11 [paper] [code] Exact Fully dynamic

   Lohan Meunier, Ying Zhao.

3. DLCR: efficient indexing for label-constrained reachability queries on large dynamic graphs. Proc. VLDB Endow. 15(8): 1645-1657 (2022) [paper] [code] Exact Fully dynamic

   Xin Chen, You Peng, Sibo Wang, Jeffrey Xu Yu.

4. Span-reachability querying in large temporal graphs. VLDB J. 31(4): 629-647 (2022) [paper] Exact Fully dynamic

   Dong Wen, Bohua Yang, Ying Zhang, Lu Qin, Dawei Cheng, Wenjie Zhang.

5. Efficient distributed reachability querying of massive temporal graphs. VLDB J. 28(6): 871-896 (2019) [paper] Exact Insert-only

   Tianming Zhang, Yunjun Gao, Lu Chen, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen.

6. Reachability and time-based path queries in temporal graphs. ICDE 2016: 145-156 [paper] Exact Insert-only

   Huanhuan Wu, Yuzhen Huang, James Cheng, Jinfeng Li, Yiping Ke.

7. Reachability queries on large dynamic graphs: a total order approach. SIGMOD Conference 2014: 1323-1334 [paper] Exact Fully dynamic

   Andy Diwen Zhu, Wenqing Lin, Sibo Wang, Xiaokui Xiao.

Shortest Path

1. DSPC: Efficiently Answering Shortest Path Counting on Dynamic Graphs. EDBT 2024: 116-128 [paper] Exact Fully dynamic

   Qingshuai Feng, You Peng, Wenjie Zhang, Xuemin Lin, Ying Zhang.

2. Towards Real-Time Counting Shortest Cycles on Dynamic Graphs: A Hub Labeling Approach. ICDE 2022: 512-524 [paper] Exact Fully dynamic

   Qingshuai Feng, You Peng, Wenjie Zhang, Ying Zhang, Xuemin Lin.

3. Distributed Processing of k Shortest Path Queries over Dynamic Road Networks. SIGMOD Conference 2020: 665-679 [paper] Exact Fully dynamic

   Ziqiang Yu, Xiaohui Yu, Nick Koudas, Yang Liu, Yifan Li, Yueting Chen, Dingyu Yang.

4. Efficient shortest path index maintenance on dynamic road networks with theoretical guarantees. Proc. VLDB Endow. 13(5): 602-615 (2020) [paper] Exact Fully dynamic

   Dian Ouyang, Long Yuan, Lu Qin, Lijun Chang, Ying Zhang, Xuemin Lin.

5. Distributed shortest path query processing on dynamic road networks. VLDB J. 26(3): 399-419 (2017) [paper] Exact Fully dynamic

   Dongxiang Zhang, Dingyu Yang, Yuan Wang, Kian-Lee Tan, Jian Cao, Heng Tao Shen.

6. Fully Dynamic Shortest-Path Distance Query Acceleration on Massive Networks. CIKM 2016: 1533-1542 [paper] Exact Fully dynamic

   Takanori Hayashi, Takuya Akiba, Ken-ichi Kawarabayashi.

7. Graph Indexing for Shortest-Path Finding over Dynamic Sub-Graphs. SIGMOD Conference 2016: 1183-1197 [paper] Exact Fully dynamic

   Mohamed S. Hassan, Walid G. Aref, Ahmed M. Aly.

Connectivity

1. Maintaining Biconnected Components in Streaming Graphs. Proc. ACM Manag. Data 4(3): 207:1-207:25 (2026) [paper] Exact Sliding window

   Zhao Lu, Dong Wen, Wentao Li, Xuemin Lin, Wenjie Zhang.

2. An Experimental Comparison of Tree-data Structures for Connectivity Queries on Fully-dynamic Undirected Graphs. Proc. ACM Manag. Data 3(1): 10:1-10:26 (2025) [paper] [code] Exact Fully dynamic Benchmark

   Qing Chen, Michael H. Böhlen, Sven Helmer.

3. Exploring the Landscape of Distributed Graph Sketching. ALENEX 2025: 133-146 [paper] Exact Fully dynamic

   David Tench, Evan T. West, Kenny Zhang, Michael A. Bender, Daniel DeLayo, Martín Farach-Colton, Gilvir Gill, Tyler Seip, Victor Zhang.

4. The Case for External Graph Sketching. ACDA 2025: 115-129 [paper] Exact Fully dynamic

   Michael A. Bender, Martín Farach-Colton, Riko Jacob, Hanna Komlós, David Tench, Evan T. West.

5. Constant-time Connectivity Querying in Dynamic Graphs. Proc. ACM Manag. Data 2(6): 230:1-230:23 (2024) [paper] Exact Fully dynamic Sliding window

   Lantian Xu, Dong Wen, Lu Qin, Rong-Hua Li, Ying Zhang, Xuemin Lin.

6. GraphZeppelin: How to Find Connected Components (Even When Graphs Are Dense, Dynamic, and Massive). ACM Trans. Database Syst. 49(3): 9:1-9:31 (2024) [paper] Exact Fully dynamic

   David Tench, Evan T. West, Victor Zhang, Michael A. Bender, Abiyaz Chowdhury, Daniel DeLayo, J. Ahmed Dellas, Martín Farach-Colton, Tyler Seip, Kenny Zhang.

7. Incremental Sliding Window Connectivity over Streaming Graphs. Proc. VLDB Endow. 17(10): 2473-2486 (2024) [paper] [code] Exact Sliding window

   Chao Zhang, Angela Bonifati, M. Tamer Özsu.

8. On Querying Historical Connectivity in Temporal Graphs. Proc. ACM Manag. Data 2(3): 157:1-157:25 (2024) [paper] Exact Insert-only Sliding window

   Jingyi Song, Dong Wen, Lantian Xu, Lu Qin, Wenjie Zhang, Xuemin Lin.

9. Towards Scalable and Practical Batch-Dynamic Connectivity. Proc. VLDB Endow. 18(3): 889-901 (2024) [paper] [code] Exact Fully dynamic

   Quinten De Man, Laxman Dhulipala, Adam Karczmarz, Jakub Łącki, Julian Shun, Zhongqi Wang.

10. Dynamic spanning trees for connectivity queries on fully-dynamic undirected graphs. Proc. VLDB Endow. 15(11): 3263-3276 (2022) [paper] [code] Exact Fully dynamic

    Qing Chen, Oded Lachish, Sven Helmer, Michael H. Böhlen.

11. GraphZeppelin: Storage-Friendly Sketching for Connected Components on Dynamic Graph Streams. SIGMOD Conference 2022: 325-339 [paper] Exact Fully dynamic

    David Tench, Evan T. West, Victor Zhang, Michael A. Bender, Abiyaz Chowdhury, J. Ahmed Dellas, Martín Farach-Colton, Tyler Seip, Kenny Zhang.

Subgraph Counting & Mining

Triangle Count

1. Streaming algorithms for triangle counting: adversarial robustness and the weighted case. Frontiers Comput. Sci. 20(1): 2001401 (2026) [paper] Approx Insert-only Fully dynamic

   Jing Cao, Yicheng Pan, Pan Peng.

2. 高精度滑动窗口模型下的图流三角形近似计数算法[J]. 软件学报, 2025, 36(9): 4349-4372. [paper] [code] Approx Sliding window

   苟向阳, 邹磊, 于旭.

3. GREAT: Generalized Reservoir Sampling based Triangle Counting Estimation over Streaming Graphs. Proc. VLDB Endow. 18(7): 2031-2043 (2025) [paper] [code] Approx Insert-only

   Siyue Wu, Dingming Wu, Sinhong Cheuk, Tsz Nam Chan, Kezhong Lu.

4. A Cloud-Edge Collaborative Framework for Distributed Triangle Counting on Graph Stream. ICC 2025: 2611-2616 [paper] Approx Insert-only

   Ruilin Hu, Chao Song, Jie Wu, Li Lu.

5. Efficient Approximate Temporal Triangle Counting in Streaming with Predictions. ECML/PKDD 2025: 244-262 [paper] Approx Insert-only

   Giorgio Venturin, Ilie Sarpe, Fabio Vandin.

6. DTC: Real-Time and Accurate Distributed Triangle Counting in Fully Dynamic Graph Streams. SRDS 2024: 198-209 [paper] [code] Approx Fully dynamic

   Wei Xuan, Yan Liang, Huawei Cao, Ning Lin, Xiaochun Ye, Dongrui Fan.

7. Fast and Accurate Triangle Counting in Graph Streams Using Predictions. ICDM 2024: 31-40 [paper] [code] Approx Fully dynamic

   Cristian Boldrin, Fabio Vandin.

8. Compact Estimator for Streaming Triangle Counting. IEEE Trans. Knowl. Data Eng. 36(8): 3712-3724 (2024) [paper] Approx Insert-only

   Jiqing Gu, Chao Song, Haipeng Dai, Li Lu, Ming Liu.

9. Efficiently Counting Triangles for Hypergraph Streams by Reservoir-Based Sampling. IEEE Trans. Knowl. Data Eng. 35(11): 11328-11341 (2023) [paper] Approx Insert-only

   Lingling Zhang, Zhiwei Zhang, Guoren Wang, Ye Yuan, Kangfei Zhao.

10. A distributed streaming framework for edge-cloud triangle counting in graph streams. Knowl. Based Syst. 278: 110878 (2023) [paper] [code] Approx Insert-only

    Xu Yang, Chao Song, Jiqing Gu, Ke Li, Hongwei Li.

11. Sliding window-based approximate triangle counting with bounded memory usage. VLDB J. 32(5): 1087-1110 (2023) [paper] [code] Approx Sliding window

    Xiangyang Gou, Lei Zou.

12. Top-k heavy weight triangles listing on graph stream. World Wide Web 26(4): 1827-1851 (2023) [paper] Approx Insert-only

    Fan Zhang, Xiangyang Gou, Lei Zou.

13. Distributed Triangle Approximately Counting Algorithms in Simple Graph Stream. ACM Trans. Knowl. Discov. Data 16(4): 79:1-79:43 (2022) [paper] [code] Approx Insert-only

    Xu Yang, Chao Song, Mengdi Yu, Jiqing Gu, Ming Liu.

14. Triangle and Four Cycle Counting with Predictions in Graph Streams. ICLR 2022 [paper] Approx Insert-only

    Justin Y. Chen, Talya Eden, Piotr Indyk, Honghao Lin, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner, David P. Woodruff, Michael Zhang.

15. Sliding Window-based Approximate Triangle Counting over Streaming Graphs with Duplicate Edges. SIGMOD Conference 2021: 645-657 [paper] [code] Approx Sliding window

    Xiangyang Gou, Lei Zou.

16. CoCoS: Fast and Accurate Distributed Triangle Counting in Graph Streams. ACM Trans. Knowl. Discov. Data 15(3): 38:1-38:30 (2021) [paper] [code] Approx Insert-only

    Kijung Shin, Euiwoong Lee, Jinoh Oh, Mohammad Hammoud, Christos Faloutsos.

17. Fast, Accurate and Provable Triangle Counting in Fully Dynamic Graph Streams. ACM Trans. Knowl. Discov. Data 14(2): 12:1-12:39 (2020) [paper] [code] Approx Fully dynamic

    Kijung Shin, Sejoon Oh, Jisu Kim, Bryan Hooi, Christos Faloutsos.

18. Temporal locality-aware sampling for accurate triangle counting in real graph streams. VLDB J. 29(6): 1501-1525 (2020) [paper] [code] Approx Fully dynamic

    Dongjin Lee, Kijung Shin, Christos Faloutsos.

19. Distributed Triangle Counting Algorithms in Simple Graph Stream. ICPADS 2019: 294-301 [paper] Approx Insert-only

    Mengdi Yu, Chao Song, Jiqing Gu, Ming Liu.

20. REPT: A Streaming Algorithm of Approximating Global and Local Triangle Counts in Parallel. ICDE 2019: 758-769 [paper] Approx Insert-only

    Pinghui Wang, Peng Jia, Yiyan Qi, Yu Sun, Jing Tao, Xiaohong Guan.

21. FURL: Fixed-memory and uncertainty reducing local triangle counting for multigraph streams. Data Min. Knowl. Discov. 33(5): 1225-1253 (2019) [paper] [code] Approx Insert-only

    Minsoo Jung, Yongsub Lim, Sunmin Lee, U Kang.

22. Tri-Fly: Distributed Estimation of Global and Local Triangle Counts in Graph Streams. PAKDD (3) 2018: 651-663 [paper] [code] Approx Insert-only

    Kijung Shin, Mohammad Hammoud, Euiwoong Lee, Jinoh Oh, Christos Faloutsos.

23. Think Before You Discard: Accurate Triangle Counting in Graph Streams with Deletions. ECML/PKDD (2) 2018: 141-157 [paper] [code] Approx Fully dynamic

    Kijung Shin, Jisu Kim, Bryan Hooi, Christos Faloutsos.

24. Memory-Efficient and Accurate Sampling for Counting Local Triangles in Graph Streams: From Simple to Multigraphs. ACM Trans. Knowl. Discov. Data 12(1): 4:1-4:28 (2018) [paper] [code] Approx Insert-only

    Yongsub Lim, Minsoo Jung, U Kang.

25. WRS: Waiting Room Sampling for Accurate Triangle Counting in Real Graph Streams. ICDM 2017: 1087-1092 [paper] [code] Approx Insert-only

    Kijung Shin.

26. Approximately Counting Triangles in Large Graph Streams Including Edge Duplicates with a Fixed Memory Usage. Proc. VLDB Endow. 11(2): 162-175 (2017) [paper] Approx Insert-only

    Pinghui Wang, Yiyan Qi, Yu Sun, Xiangliang Zhang, Jing Tao, Xiaohong Guan.

27. TRIÈST: Counting Local and Global Triangles in Fully Dynamic Streams with Fixed Memory Size. ACM Trans. Knowl. Discov. Data 11(4): 43:1-43:50 (2017) [paper] [code] Approx Fully dynamic

    Lorenzo De Stefani, Alessandro Epasto, Matteo Riondato, Eli Upfal.

28. TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size. KDD 2016: 825-834 [paper] [code] Approx Fully dynamic

    Lorenzo De Stefani, Alessandro Epasto, Matteo Riondato, Eli Upfal.

29. MASCOT: Memory-efficient and Accurate Sampling for Counting Local Triangles in Graph Streams. KDD 2015: 685-694 [paper] [code] Approx Insert-only

    Yongsub Lim, U Kang.

30. A Space-Efficient Streaming Algorithm for Estimating Transitivity and Triangle Counts Using the Birthday Paradox. ACM Trans. Knowl. Discov. Data 9(3): 15:1-15:21 (2015) [paper] Approx Insert-only

    Madhav Kumar Jha, Comandur Seshadhri, Ali Pinar.

31. A space efficient streaming algorithm for triangle counting using the birthday paradox. KDD 2013: 589-597 [paper] Approx Insert-only

    Madhav Kumar Jha, Comandur Seshadhri, Ali Pinar.

32. Counting and Sampling Triangles from a Graph Stream. Proc. VLDB Endow. 6(14): 1870-1881 (2013) [paper] Approx Insert-only Sliding window

    A. Pavan, Kanat Tangwongsan, Srikanta Tirthapura, Kun-Lung Wu.

33. Parallel triangle counting in massive streaming graphs. CIKM 2013: 781-786 [paper] Approx Insert-only

    Kanat Tangwongsan, A. Pavan, Srikanta Tirthapura.

Butterfly Count

1. Counting Butterflies over Streaming Bipartite Graphs with Duplicate Edges. IEEE Trans. Knowl. Data Eng. 38(7): 4200-4212 (2026) [paper] Approx Insert-only

   Lingkai Meng, Long Yuan, Xuemin Lin, Chengjie Li, Kai Wang, Wenjie Zhang.

2. FABLE: Approximate Butterfly Counting in Bipartite Graph Stream with Duplicate Edges. CIKM 2024: 2158-2167 [paper] Approx Insert-only

   Guozhang Sun, Yuhai Zhao, Yuan Li.

3. Counting Butterflies in Fully Dynamic Bipartite Graph Streams. ICDE 2024: 2917-2930 [paper] Approx Fully dynamic

   Serafeim Papadias, Zoi Kaoudi, Varun Pandey, Jorge-Arnulfo Quiané-Ruiz, Volker Markl.

4. Efficient Temporal Butterfly Counting and Enumeration on Temporal Bipartite Graphs. Proc. VLDB Endow. 17(4): 657-670 (2023) [paper] [code] Exact Fully dynamic

   Xin-Wei Cai, Xiangyu Ke, Kai Wang, Lu Chen, Tianming Zhang, Qing Liu, Yunjun Gao.

5. Approximately Counting Butterflies in Large Bipartite Graph Streams. IEEE Trans. Knowl. Data Eng. 34(12): 5621-5635 (2022) [paper] Approx Insert-only

   Rundong Li, Pinghui Wang, Peng Jia, Xiangliang Zhang, Junzhou Zhao, Jing Tao, Ye Yuan, Xiaohong Guan.

6. sGrapp: Butterfly Approximation in Streaming Graphs. ACM Trans. Knowl. Discov. Data 16(4): 76:1-76:43 (2022) [paper] [code] Approx Insert-only

   Aida Sheshbolouki, M. Tamer Özsu.

7. FLEET: Butterfly Estimation from a Bipartite Graph Stream. CIKM 2019: 1201-1210 [paper] [code] Approx Insert-only Sliding window

   Seyed-Vahid Sanei-Mehri, Yu Zhang, Ahmet Erdem Sariyüce, Srikanta Tirthapura.

Subgraph Counting

1. An Efficient Streaming Algorithm for Approximating Graphlet Distributions. Proc. ACM Manag. Data 4(3): 133:1-133:26 (2026) [paper] Approx Insert-only

   Marco Bressan, T-H. Hubert Chan, Qipeng Kuang, Mauro Sozio.

2. Four-Cycle Counting in Low-Degeneracy Graph Streams. KDD 2026: 983-994 [paper] Approx Insert-only

   Sebastian Lüderssen, Stefan Neumann, Pan Peng.

3. StreamSC: A Learning-Based Framework for Efficient Subgraph Counting in Stream Graphs. ICDE 2025: 4694-4698 [paper] Approx Fully dynamic

   Zhen Xie, Xiang Zhao.

4. Neural subgraph counting on stream graphs via localized updates and monotonic learning. PLoS ONE 20(10): e0334724 (2025) [paper] Approx Fully dynamic

   Zhen Xie, Wenzhe Hou, Feiyang Wu, Hao Xu.

5. SGES: A General and Space-efficient Framework for Graphlet Counting in Graph Streams. CIKM 2024: 2817-2826 [paper] Approx Insert-only

   Chen Yang, Lailong Luo, Yuliang Lu, Chu Huang, Qianzhen Zhang, Guozheng Yang, Deke Guo.

6. PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining. DAC 2023: 1-6 [paper] Exact Insert-only

   Hao Qi, Yu Zhang, Ligang He, Kang Luo, Jun Huang, Haoyu Lu, Jin Zhao, Hai Jin.

7. Reinforcement Learning Enhanced Weighted Sampling for Accurate Subgraph Counting on Fully Dynamic Graph Streams. ICDE 2023: 1084-1097 [paper] [code] Approx Fully dynamic

   Kaixin Wang, Cheng Long, Da Yan, Jie Zhang, H. V. Jagadish.

8. Efficient Sampling Algorithms for Approximate Motif Counting in Temporal Graph Streams. CoRR abs/2211.12101 (2022) [paper] Approx Insert-only

   Jingjing Wang, Yanhao Wang, Wenjun Jiang, Yuchen Li, Kian-Lee Tan.

Frequent Subgraph Mining

1. Discovering Persistent Subgraph Patterns over Streaming Graphs. DASFAA 2023: 154-171 [paper] Approx Sliding window

   Chu Huang, Qianzhen Zhang, Deke Guo, Xiang Zhao.

2. Mining Top-k Frequent Patterns over Streaming Graphs. DASFAA 2023: 199-216 [paper] Approx Insert-only

   Xi Wang, Qianzhen Zhang, Deke Guo, Xiang Zhao.

3. TipTap: Approximate Mining of Frequent k-Subgraph Patterns in Evolving Graphs. ACM Trans. Knowl. Discov. Data 15(3): 48:1-48:35 (2021) [paper] Approx Fully dynamic

   Muhammad Anis Uddin Nasir, Çiğdem Aslay, Gianmarco De Francisci Morales, Matteo Riondato.

4. Mining Persistent Activity in Continually Evolving Networks. KDD 2020: 934-944 [paper] [code] Exact Fully dynamic

   Caleb Belth, Xinyi Zheng, Danai Koutra.

5. Mining Frequent Patterns in Evolving Graphs. CIKM 2018: 923-932 [paper] Approx Fully dynamic

   Çiğdem Aslay, Muhammad Anis Uddin Nasir, Gianmarco De Francisci Morales, Aristides Gionis.

6. On dense pattern mining in graph streams. Proc. VLDB Endow. 3(1-2): 975-984 (2010) [paper] Approx Insert-only

   Charu C. Aggarwal, Yao Li, Philip S. Yu, Ruoming Jin.

Cycle, Clique & Biclique

1. Maximal η-clique maintenance over uncertain graph streams. Inf. Sci. 717: 122318 (2025) [paper] [code] Exact Fully dynamic

   Ziyi Ma, Liqing Wang, Jianye Yang, Xu Zhou, Kenli Li, Cuiyun Gao.

2. TopK-BC: Efficient Maintenance of Top k (p,q)-bicliques over Streaming Bipartite Graphs. ICDE 2025: 3696-3709 [paper] Exact Sliding window

   Xin Deng, Zheng Qin, Peng Peng, Hui Zhou.

3. Efficient maintenance for maximal bicliques in bipartite graph streams. World Wide Web 25(2): 857-877 (2022) [paper] Exact Fully dynamic

   Ziyi Ma, Yuling Liu, Yikun Hu, Jianye Yang, Chubo Liu, Huadong Dai.

4. Real-time Constrained Cycle Detection in Large Dynamic Graphs. Proc. VLDB Endow. 11(12): 1876-1888 (2018) [paper] Exact Sliding window

   Xiafei Qiu, Wubin Cen, Zhengping Qian, You Peng, Ying Zhang, Xuemin Lin, Jingren Zhou.

Cohesive Subgraphs & Communities

Core & Tip Decomposition

1. Accelerating D-Core Maintenance over Dynamic Directed Graphs. ICDE 2025: 696-709 [paper] Exact Fully dynamic

   Xuankun Liao, Qing Liu, Jiaxin Jiang, Byron Choi, Bingsheng He, Jianliang Xu.

2. Efficient Temporal Edge-Core Maintenance in Streaming Graphs. Proc. VLDB Endow. 19(4): 726-739 (2025) [paper] [code] Exact Fully dynamic

   Tongfeng Weng, Mo Sha, Xu Zhou, Jingjing Lu, Kenli Li, Kian-Lee Tan.

3. I/O efficient core maintenance in large graphs. Expert Syst. Appl. 288: 128234 (2025) [paper] Exact Fully dynamic

   Jie Dai, Yixiao Liu, Yuan Li, Fazal Wahab, Yuhai Zhao.

4. Maintaining Top-t Cores in Dynamic Graphs. IEEE Trans. Knowl. Data Eng. 36(9): 4766-4780 (2024) [paper] Exact Fully dynamic

   Yikai Zhang, Jeffrey Xu Yu, Ying Zhang, Lu Qin.

5. Simplified algorithms for order-based core maintenance. J. Supercomput. 80(13): 19592-19623 (2024) [paper] [code] Exact Fully dynamic

   Bin Guo, Emil Sekerinski.

6. Core maintenance for hypergraph streams. World Wide Web 26(5): 3709-3733 (2023) [paper] Exact Fully dynamic

   Qi Luo, Dongxiao Yu, Zhipeng Cai, Yanwei Zheng, Xiuzhen Cheng, Xuemin Lin.

7. Distributed Approaches to Butterfly Analysis on Large Dynamic Bipartite Graphs. IEEE Trans. Parallel Distributed Syst. 34(2): 431-445 (2023) [paper] Exact Fully dynamic

   Tongfeng Weng, Xu Zhou, Kenli Li, Kian-Lee Tan, Keqin Li.

8. Incremental Graph Computation: Anchored Vertex Tracking in Dynamic Social Networks. IEEE Trans. Knowl. Data Eng. 35(7): 7030-7044 (2023) [paper] Approx Snapshots

   Taotao Cai, Shuiqiao Yang, Jianxin Li, Quan Z. Sheng, Jian Yang, Xin Wang, Wei Emma Zhang, Longxiang Gao.

9. Quantifying Node Importance over Network Structural Stability. KDD 2023: 3217-3228 [paper] Exact Fully dynamic

   Fan Zhang, Qingyuan Linghu, Jiadong Xie, Kai Wang, Xuemin Lin, Wenjie Zhang.

10. Hierarchical core maintenance on large dynamic graphs. Proc. VLDB Endow. 14(5): 757-770 (2021) [paper] Exact Fully dynamic

    Zhe Lin, Fan Zhang, Xuemin Lin, Wenjie Zhang, Zhihong Tian.

11. Parallel and Streaming Algorithms for K-Core Decomposition. ICML 2018: 1397-1406 [paper] Approx Insert-only

    Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni.

12. A Fast Order-Based Approach for Core Maintenance. ICDE 2017: 337-348 [paper] Exact Fully dynamic

    Yikai Zhang, Jeffrey Xu Yu, Ying Zhang, Lu Qin.

13. Incremental k-core decomposition: algorithms and evaluation. VLDB J. 25(3): 425-447 (2016) [paper] Exact Fully dynamic

    Ahmet Erdem Sariyüce, Buğra Gedik, Gabriela Jacques-Silva, Kun-Lung Wu, Ümit V. Çatalyürek.

14. Streaming algorithms for k-core decomposition. Proc. VLDB Endow. 6(6): 433-444 (2013) [paper] Exact Fully dynamic

    Ahmet Erdem Sariyüce, Buğra Gedik, Gabriela Jacques-Silva, Kun-Lung Wu, Ümit V. Çatalyürek.

Dense Subgraph

1. New Parallel and Streaming Algorithms for Directed Densest Subgraph. NeurIPS 2025: 40441-40465 [paper] Approx Insert-only

   Slobodan Mitrovic, Theodore Pan, Mahdi Qaempanah, Mohammad Amin Raeisi.

2. Approximate Anchored Densest Subgraph Search on Large Static and Dynamic Graphs. Proc. VLDB Endow. 18(3): 623-636 (2024) [paper] [code] Approx Fully dynamic

   Qi Zhang, Yalong Zhang, Rong-Hua Li, Guoren Wang.

3. Faster Streaming and Scalable Algorithms for Finding Directed Dense Subgraphs in Large Graphs. ICML 2024: 35876-35891 [paper] Approx Insert-only

   Slobodan Mitrovic, Theodore Pan.

4. Spade+: A Generic Real-Time Fraud Detection Framework on Dynamic Graphs. IEEE Trans. Knowl. Data Eng. 36(11): 7058-7073 (2024) [paper] Approx Fully dynamic

   Jiaxin Jiang, Yuhang Chen, Bingsheng He, Min Chen, Jia Chen.

5. Spade: A Real-Time Fraud Detection Framework on Evolving Graphs. Proc. VLDB Endow. 16(3): 461-469 (2022) [paper] [code] Approx Insert-only

   Jiaxin Jiang, Yuan Li, Bingsheng He, Bryan Hooi, Jia Chen, Johan Kok Zhi Kang.

Community Detection

1. Communities in Streaming Graphs: Small Space Data Structure, Benchmark Data Generation, and Linear Algorithm. ACM Trans. Knowl. Discov. Data 19(5): 110:1-110:24 (2025) [paper] [code] Approx Insert-only

   Shubham Gupta, Suman Kundu.

2. Streaming Local Community Detection Through Approximate Conductance. IEEE Trans. Big Data 10(1): 12-22 (2024) [paper] Approx Insert-only

   Meng Wang, Yanhao Yang, David Bindel, Kun He.

3. Rapid Detection of Local Communities in Graph Streams. IEEE Trans. Knowl. Data Eng. 34(5): 2375-2386 (2022) [paper] Approx Insert-only

   Panagiotis Liakos, Katia Papakonstantinopoulou, Alexandros Ntoulas, Alex Delis.

4. Community Detection in Graph Streams by Pruning Zombie Nodes. PAKDD 2017: 574-585 [paper] Approx Insert-only

   Yue Ding, Ling Huang, Chang-Dong Wang, Dong Huang.

5. Dynamic Community Detection in Weighted Graph Streams. SDM 2013: 151-161 [paper] Approx Insert-only

   Chang-Dong Wang, Jian-Huang Lai, Philip S. Yu.

Community Search

1. Finding Time-Proximity Communities in Temporal Heterogeneous Information Networks. Proc. VLDB Endow. 18(13): 5740-5752 (2025) [paper] [code] Exact Insert-only

   Yifu Tang, Chengfei Liu, Lu Chen, Rui Zhou, Jianxin Li.

2. Truss-Based Community Search over Streaming Directed Graphs. Proc. VLDB Endow. 17(8): 1816-1829 (2024) [paper] Exact Sliding window

   Xuankun Liao, Qing Liu, Xin Huang, Jianliang Xu.

3. Discovering Significant Communities on Bipartite Graphs: An Index-based Approach. IEEE Trans. Knowl. Data Eng. 35(3): 2471-2485 (2023) [paper] Exact Fully dynamic

   Kai Wang, Wenjie Zhang, Ying Zhang, Lu Qin, Yuting Zhang.

4. Searching Personalized k-Wing in Bipartite Graphs. IEEE Trans. Knowl. Data Eng. 35(8): 8515-8528 (2023) [paper] Exact Fully dynamic

   Aman Abidi, Lu Chen, Rui Zhou, Chengfei Liu.

5. Reliable Community Search in Dynamic Networks. Proc. VLDB Endow. 15(11): 2826-2838 (2022) [paper] [code] Exact Fully dynamic

   Yifu Tang, Jianxin Li, Nur Al Hasan Haldar, Ziyu Guan, Jiajie Xu, Chengfei Liu.

Graph Clustering

1. Efficient processing of streaming graphs for evolution-aware clustering. CIKM 2013: 319-328 [paper] Approx Fully dynamic Sliding window

   Mindi Yuan, Kun-Lung Wu, Gabriela Jacques-Silva, Yi Lu.

2. Clustering Streaming Graphs. ICDCS 2012: 466-475 [paper] Approx Fully dynamic Sliding window

   Ahmed Eldawy, Rohit Khandekar, Kun-Lung Wu.

3. On Clustering Graph Streams. SDM 2010: 478-489 [paper] Approx Insert-only

   Charu C. Aggarwal, Yuchen Zhao, Philip S. Yu.

Graph Analytics

1. Identifying influential vertices with top-K largest temporal katz centralities in a streaming graph using constant memory. Expert Syst. Appl. 296: 128943 (2026) [paper] Approx Insert-only

   Jiaming Zhang, Qingjun Xiao, Jun Ma, Liukun He, Qifan Zhang, Guang Cheng.

2. Semi-Streaming Algorithms for Hypergraph Matching. ESA 2025: 79:1-79:19 [paper] [code] Approx Insert-only

   Henrik Reinstädtler, S. M. Ferdous, Alex Pothen, Bora Uçar, Christian Schulz.

3. Influence Maximization via Vertex Countering. Proc. VLDB Endow. 17(6): 1297-1309 (2024) [paper] [code] Approx Fully dynamic

   Jiadong Xie, Zehua Chen, Deming Chu, Fan Zhang, Xuemin Lin, Zhihong Tian.

4. Querying Structural Diversity in Streaming Graphs. Proc. VLDB Endow. 17(5): 1034-1046 (2024) [paper] [code] Exact Insert-only Sliding window

   Kaiyu Chen, Dong Wen, Wenjie Zhang, Ying Zhang, Xiaoyang Wang, Xuemin Lin.

5. Space-efficient random walks on streaming graphs. Proc. VLDB Endow. 16(2): 356-368 (2022) [paper] [code] Exact Fully dynamic

   Serafeim Papadias, Zoi Kaoudi, Jorge-Arnulfo Quiané-Ruiz, Volker Markl.

6. A Fast Sketch Method for Mining User Similarities Over Fully Dynamic Graph Streams. ICDE 2019: 1682-1685 [paper] Approx Fully dynamic

   Peng Jia, Pinghui Wang, Jing Tao, Xiaohong Guan.

7. Sampling for Approximate Bipartite Network Projection. IJCAI 2018: 3286-3292 [paper] Approx Insert-only

   Nesreen K. Ahmed, Nick Duffield, Liangzhen Xia.

8. Fully dynamic betweenness centrality maintenance on massive networks. Proc. VLDB Endow. 9(2): 48-59 (2015) [paper] Approx Fully dynamic

   Takanori Hayashi, Takuya Akiba, Yuichi Yoshida.

Anomaly & Event Detection

Anomaly Detection

1. Anomaly Detection of Interaction Behaviors in Streaming Graphs. WWW 2026: 879-890 [paper] Approx Fully dynamic

   Shuai Ren, Fan Zhang, Bolin Wang, Xiang Zhao, Zhihong Tian.

2. STG-DGR: Fraud Detection on Streaming Transaction Graphs with Diffusion-based Generative Replay. WWW 2026: 742-753 [paper] Insert-only

   Rui Ou, Kun Zhu, Nana Zhang, Jiangtong Li, Chaochao Chen, Changjun Jiang.

3. FiFrauD: Unsupervised Financial Fraud Detection in Dynamic Graph Streams. ACM Trans. Knowl. Discov. Data 18(5): 111:1-111:29 (2024) [paper] Approx Snapshots

   Samira Khodabandehlou, Alireza Hashemi Golpayegani.

4. SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning. KDD 2024: 1506-1517 [paper] Insert-only

   Jongha Lee, Sunwoo Kim, Kijung Shin.

5. Anonymous Edge Representation for Inductive Anomaly Detection in Dynamic Bipartite Graph. Proc. VLDB Endow. 16(5): 1154-1167 (2023) [paper] [code] Insert-only

   Lanting Fang, Kaiyu Feng, Jie Gui, Shanshan Feng, Aiqun Hu.

6. Example-based explanations for streaming fraud detection on graphs. Inf. Sci. 621: 319-340 (2023) [paper] Insert-only

   Thanh Tam Nguyen, Thanh Cong Phan, Hien Thu Pham, Thanh Thi Nguyen, Jun Jo, Quoc Viet Hung Nguyen.

7. Sketch-Based Anomaly Detection in Streaming Graphs. KDD 2023: 93-104 [paper] [code] Approx Insert-only

   Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip S. Yu, Bryan Hooi.

8. MonLAD: Money Laundering Agents Detection in Transaction Streams. WSDM 2022: 976-986 [paper] [code] Approx Insert-only Sliding window

   Xiaobing Sun, Wenjie Feng, Shenghua Liu, Yuyang Xie, Siddharth Bhatia, Bryan Hooi, Wenhan Wang, Xueqi Cheng.

9. Real-Time Anomaly Detection in Edge Streams. ACM Trans. Knowl. Discov. Data 16(4): 75:1-75:22 (2022) [paper] [code] Approx Insert-only

   Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos.

10. F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams. WSDM 2021: 589-597 [paper] [code] Approx Insert-only

    Yen-Yu Chang, Pan Li, Rok Sosic, M. H. Afifi, Marco Schweighauser, Jure Leskovec.

11. MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams. AAAI 2020: 3242-3249 [paper] [code] Approx Insert-only

    Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos.

12. Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach. KDD 2019: 647-657 [paper] [code] Approx Fully dynamic

    Minji Yoon, Bryan Hooi, Kijung Shin, Christos Faloutsos.

13. SedanSpot: Detecting Anomalies in Edge Streams. ICDM 2018: 953-958 [paper] [code] Approx Insert-only

    Dhivya Eswaran, Christos Faloutsos.

14. SpotLight: Detecting Anomalies in Streaming Graphs. KDD 2018: 1378-1386 [paper] Approx Snapshots

    Dhivya Eswaran, Christos Faloutsos, Sudipto Guha, Nina Mishra.

15. Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs. KDD 2016: 1035-1044 [paper] Approx Insert-only

    Emaad Manzoor, Sadegh M. Milajerdi, Leman Akoglu.

16. On Anomalous Hotspot Discovery in Graph Streams. ICDM 2013: 1271-1276 [paper] Approx Insert-only

    Weiren Yu, Charu C. Aggarwal, Shuai Ma, Haixun Wang.

17. Outlier detection in graph streams. ICDE 2011: 399-409 [paper] Approx Insert-only

    Charu C. Aggarwal, Yuchen Zhao, Philip S. Yu.

Burst & Event Detection

1. RUSH: Real-Time Burst Subgraph Detection in Dynamic Graphs. Proc. VLDB Endow. 17(11): 3657-3665 (2024) [paper] [code] Approx Insert-only

   Yuhang Chen, Jiaxin Jiang, Shixuan Sun, Bingsheng He, Min Chen.

2. Discovering Bursting Patterns over Streaming Graphs. DASFAA 2022: 441-458 [paper] Approx Insert-only

   Qianzhen Zhang, Deke Guo, Xiang Zhao.

3. Tracking triadic cardinality distributions for burst detection in high-speed graph streams. Knowl. Inf. Syst. 63(4): 939-969 (2021) [paper] Approx Insert-only

   Junzhou Zhao, Pinghui Wang, Zhouguo Chen, Jianwei Ding, John C. S. Lui, Don Towsley, Xiaohong Guan.

4. Event pattern discovery by keywords in graph streams. IEEE BigData 2017: 982-987 [paper] Exact Snapshots

   Mohammad Hossein Namaki, Peng Lin, Yinghui Wu.

Concept Drift

1. An Approach For Concept Drift Detection in a Graph Stream Using Discriminative Subgraphs. ACM Trans. Knowl. Discov. Data 14(6): 70:1-70:25 (2020) [paper] [code] Approx Sliding window

   Ramesh Paudel, William Eberle.

2. Concept Drift and Anomaly Detection in Graph Streams. IEEE Trans. Neural Networks Learn. Syst. 29(11): 5592-5605 (2018) [paper] Approx Insert-only

   Daniele Zambon, Cesare Alippi, Lorenzo Livi.

Learning on Streaming Graphs

Representation Learning

1. Incremental GNN Embedding Computation on Streaming Graphs. ICDE 2026: 2349-2362 [paper] Fully dynamic

   Qiange Wang, Haoran Lv, Yanfeng Zhang, Weng-Fai Wong, Bingsheng He.

2. Spatio-Temporal Fusion in Graph Neural Network for Streaming Knowledge Tracing. DASFAA 2026: 136-153 [paper] Insert-only

   Yi-Fei Wen, Hang Liang, Carl Yang, Wen-Bo Xie, Yajun Du, Xianyong Li, Yan-Li Lee.

3. InkStream: Instantaneous GNN Inference on Dynamic Graphs via Incremental Update. IPDPS 2025: 1273-1285 [paper] [code] Fully dynamic

   Dan Wu, Zhaoying Li, Tulika Mitra.

4. Ripple: Scalable Incremental GNN Inferencing on Large Streaming Graphs. ICDCS 2025: 857-867 [paper] Fully dynamic

   Pranjal Naman, Yogesh Simmhan.

5. D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks. Proc. VLDB Endow. 17(11): 2764-2777 (2024) [paper] [code] Fully dynamic

   Rustam Guliyev, Aparajita Haldar, Hakan Ferhatosmanoglu.

6. Reducing Resource Usage for Continuous Model Updating and Predictive Query Answering in Graph Streams. ICDE 2024: 2653-2666 [paper] Snapshots

   Qu Liu, Adam King, Tingjian Ge.

7. NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams. Proc. VLDB Endow. 17(3): 455-468 (2023) [paper] [code] Insert-only

   Chaoyi Chen, Dechao Gao, Yanfeng Zhang, Qiange Wang, Zhenbo Fu, Xuecang Zhang, Junhua Zhu, Yu Gu, Ge Yu.

8. Streaming Graph Embeddings via Incremental Neighborhood Sketching. IEEE Trans. Knowl. Data Eng. 35(5): 5296-5310 (2023) [paper] Insert-only

   Dingqi Yang, Bingqing Qu, Jie Yang, Liang Wang, Philippe Cudré-Mauroux.

9. RL2: A Call for Simultaneous Representation Learning and Rule Learning for Graph Streams. KDD 2022: 1109-1119 [paper] Insert-only

   Qu Liu, Tingjian Ge.

10. Streaming Graph Neural Networks with Generative Replay. KDD 2022: 1878-1888 [paper] [code] Snapshots

    Junshan Wang, Wenhao Zhu, Guojie Song, Liang Wang.

11. T-GCN: A Sampling Based Streaming Graph Neural Network System with Hybrid Architecture. PACT 2022: 69-82 [paper] Insert-only

    Chengying Huan, Shuaiwen Leon Song, Yongchao Liu, Heng Zhang, Hang Liu, Charles He, Kang Chen, Jinlei Jiang, Yongwei Wu.

12. Streaming Dynamic Graph Neural Networks for Continuous-Time Temporal Graph Modeling. ICDM 2021: 1361-1366 [paper] Insert-only

    Sheng Tian, Tao Xiong, Leilei Shi.

13. Streaming Graph Neural Networks via Continual Learning. CIKM 2020: 1515-1524 [paper] Snapshots

    Junshan Wang, Guojie Song, Yi Wu, Liang Wang.

14. Streaming Graph Neural Networks. SIGIR 2020: 719-728 [paper] [code] Insert-only

    Yao Ma, Ziyi Guo, Zhaocun Ren, Jiliang Tang, Dawei Yin.

Node & Graph Classification

1. Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts. ICDE 2025: 2521-2534 [paper] Insert-only

   Jongha Lee, Taehyung Kwon, Heechan Moon, Kijung Shin.

2. PUMA: Efficient Continual Graph Learning for Node Classification With Graph Condensation. IEEE Trans. Knowl. Data Eng. 37(1): 449-461 (2025) [paper] [code] Snapshots

   Yilun Liu, Ruihong Qiu, Yanran Tang, Hongzhi Yin, Zi Huang.

3. CaT: Balanced Continual Graph Learning with Graph Condensation. ICDM 2023: 1157-1162 [paper] [code] Snapshots

   Yilun Liu, Ruihong Qiu, Zi Huang.

4. On Supervised Change Detection in Graph Streams. SDM 2020: 289-297 [paper] Sliding window

   Charu C. Aggarwal, Yao Li, Philip S. Yu.

5. Hashing for Adaptive Real-Time Graph Stream Classification With Concept Drifts. IEEE Trans. Cybern. 48(5): 1591-1604 (2018) [paper] Insert-only

   Lianhua Chi, Bin Li, Xingquan Zhu, Shirui Pan, Ling Chen.

6. Graph Ensemble Boosting for Imbalanced Noisy Graph Stream Classification. IEEE Trans. Cybern. 45(5): 954-968 (2015) [paper] Insert-only

   Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang.

7. Graph stream classification using labeled and unlabeled graphs. ICDE 2013: 398-409 [paper] Insert-only

   Shirui Pan, Xingquan Zhu, Chengqi Zhang, Philip S. Yu.

8. On Classification of Graph Streams. SDM 2011: 652-663 [paper] Insert-only

   Charu C. Aggarwal.

Link Prediction & Recommendation

1. GPL4SRec: Graph Multi-Level Aware Prompt Learning for Streaming Recommendation. IJCAI 2025: 2713-2721 [paper] Snapshots

   Hao Cang, Huanhuan Yuan, Jiaqing Fan, Lei Zhao, Guanfeng Liu, Pengpeng Zhao.

2. GPT4Rec: Graph Prompt Tuning for Streaming Recommendation. SIGIR 2024: 1774-1784 [paper] Snapshots

   Peiyan Zhang, Yuchen Yan, Xi Zhang, Liying Kang, Chaozhuo Li, Feiran Huang, Senzhang Wang, Sunghun Kim.

3. GraphPro: Graph Pre-training and Prompt Learning for Recommendation. WWW 2024: 3690-3699 [paper] [code] Snapshots

   Yuhao Yang, Lianghao Xia, Da Luo, Kangyi Lin, Chao Huang.

4. Dynamically Expandable Graph Convolution for Streaming Recommendation. WWW 2023: 1457-1467 [paper] Snapshots

   Bowei He, Xu He, Yingxue Zhang, Ruiming Tang, Chen Ma.

5. The Link Regression Problem in Graph Streams. IEEE BigData 2019: 1088-1095 [paper] Insert-only

   Bowen Dong, Charu C. Aggarwal, Philip S. Yu.

6. Streaming Link Prediction on Dynamic Attributed Networks. WSDM 2018: 369-377 [paper] Insert-only

   Jundong Li, Kewei Cheng, Liang Wu, Huan Liu.

7. Link prediction in graph streams. ICDE 2016: 553-564 [paper] Insert-only

   Peixiang Zhao, Charu C. Aggarwal, Gewen He.

Generation & Forecasting

1. sGrow: Explaining the Scale-Invariant Strength Assortativity of Streaming Butterflies. ACM Trans. Web 17(3): 24:1-24:46 (2023) [paper] Insert-only

   Aida Sheshbolouki, M. Tamer Özsu.

2. TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning. IJCAI 2021: 3620-3626 [paper] [code] Snapshots

   Xu Chen, Junshan Wang, Kunqing Xie.