• Yan D, Yuan L, Ahmad A and Adhikari S. Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. (5547-5550).

    https://doi.org/10.1145/3627673.3679101

  • Bonifati A, Özsu M, Tian Y, Voigt H, Yu W and Zhang W. The Future of Graph Analytics. Companion of the 2024 International Conference on Management of Data. (544-545).

    https://doi.org/10.1145/3626246.3658369

  • Zhang Z, Lu Y, Zheng W and Lin X. (2024). A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and Interaction. Proceedings of the ACM on Management of Data. 2:1. (1-29). Online publication date: 12-Mar-2024.

    https://doi.org/10.1145/3639315

  • Gévay G, Soto J and Markl V. (2021). Handling Iterations in Distributed Dataflow Systems. ACM Computing Surveys. 54:9. (1-38). Online publication date: 31-Dec-2022.

    https://doi.org/10.1145/3477602

  • Khalil J, Yan D, Guo G and Yuan L. (2021). Parallel mining of large maximal quasi-cliques. The VLDB Journal — The International Journal on Very Large Data Bases. 31:4. (649-674). Online publication date: 1-Jul-2022.

    https://doi.org/10.1007/s00778-021-00712-2

  • Yan D, Guo G, Khalil J, Özsu M, Ku W and Lui J. (2021). G-thinker: a general distributed framework for finding qualified subgraphs in a big graph with load balancing. The VLDB Journal — The International Journal on Very Large Data Bases. 31:2. (287-320). Online publication date: 1-Mar-2022.

    https://doi.org/10.1007/s00778-021-00688-z

  • Smagulova A and Deutsch A. Vertex-centric Parallel Computation of SQL Queries. Proceedings of the 2021 International Conference on Management of Data. (1664-1677).

    https://doi.org/10.1145/3448016.3457314

  • Lu S, Sun S, Paul J, Li Y and He B. Cache-Efficient Fork-Processing Patterns on Large Graphs. Proceedings of the 2021 International Conference on Management of Data. (1208-1221).

    https://doi.org/10.1145/3448016.3457253

  • Ko S, Lee T, Hong K, Lee W, Seo I, Seo J and Han W. iTurboGraph. Proceedings of the 2021 International Conference on Management of Data. (977-990).

    https://doi.org/10.1145/3448016.3457243

  • Dhulipala L, Blelloch G and Shun J. (2021). Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable. ACM Transactions on Parallel Computing. 8:1. (1-70). Online publication date: 31-Mar-2021.

    https://doi.org/10.1145/3434393

  • Liu H, Jiang Y, Fan H, Wang X and Zhao K. (2020). Visualization Analysis of Knowledge Network Research Based on Mapping Knowledge. Journal of Signal Processing Systems. 93:2-3. (333-344). Online publication date: 1-Mar-2021.

    https://doi.org/10.1007/s11265-020-01595-2

  • De Leo D and Boncz P. (2021). Teseo and the analysis of structural dynamic graphs. Proceedings of the VLDB Endowment. 14:6. (1053-1066). Online publication date: 1-Feb-2021.

    https://doi.org/10.14778/3447689.3447708

  • Zhang Y, Wang H, Jia M, Wang J, Li D, Xue G and Tan K. (2020). TopoX: Topology Refactorization for Minimizing Network Communication in Graph Computations. IEEE/ACM Transactions on Networking. 28:6. (2768-2782). Online publication date: 1-Dec-2020.

    https://doi.org/10.1109/TNET.2020.3020813

  • Behnezhad S, Dhulipala L, Esfandiari H, Lacki J, Mirrokni V and Schudy W. (2020). Parallel graph algorithms in constant adaptive rounds. Proceedings of the VLDB Endowment. 13:13. (3588-3602). Online publication date: 1-Sep-2020.

    https://doi.org/10.14778/3424573.3424579

  • Fan W, Lu P, Yu W, Xu J, Yin Q, Luo X, Zhou J and Jin R. (2020). Adaptive Asynchronous Parallelization of Graph Algorithms. ACM Transactions on Database Systems. 45:2. (1-45). Online publication date: 30-Jun-2020.

    https://doi.org/10.1145/3397491

  • Dhulipala L, Shi J, Tseng T, Blelloch G and Shun J. The Graph Based Benchmark Suite (GBBS). Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA). (1-8).

    https://doi.org/10.1145/3398682.3399168

  • Shun J. Practical parallel hypergraph algorithms. Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. (232-249).

    https://doi.org/10.1145/3332466.3374527

  • Chen H, Li C, Fang J, Huang C, Cheng J, Zhang J, Hou Y and Yan X. Grasper. Proceedings of the ACM Symposium on Cloud Computing. (87-100).

    https://doi.org/10.1145/3357223.3362715

  • Yang Y, Yan D, Zhou S and Guo G. Parallel Clique-Like Subgraph Counting and Listing. Conceptual Modeling. (484-497).

    https://doi.org/10.1007/978-3-030-33223-5_40

  • Meng K, Li J, Tan G and Sun N. A pattern based algorithmic autotuner for graph processing on GPUs. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming. (201-213).

    https://doi.org/10.1145/3293883.3295716

  • Fan W, Yu W, Xu J, Zhou J, Luo X, Yin Q, Lu P, Cao Y and Xu R. (2018). Parallelizing Sequential Graph Computations. ACM Transactions on Database Systems. 43:4. (1-39). Online publication date: 16-Dec-2018.

    https://doi.org/10.1145/3282488

  • Zhang Y, Yang M, Baghdadi R, Kamil S, Shun J and Amarasinghe S. (2018). GraphIt: a high-performance graph DSL. Proceedings of the ACM on Programming Languages. 2:OOPSLA. (1-30). Online publication date: 24-Oct-2018.

    https://doi.org/10.1145/3276491

  • Cheng Y, Wang F, Jiang H, Hua Y, Feng D, Zhang L and Zhou J. (2018). A communication-reduced and computation-balanced framework for fast graph computation. Frontiers of Computer Science: Selected Publications from Chinese Universities. 12:5. (887-907). Online publication date: 1-Oct-2018.

    https://doi.org/10.1007/s11704-018-6400-1

  • Fan W, Cao Y, Xu J, Yu W, Wu Y, Tian C, Jiang J and Zhang B. (2018). From Think Parallel to Think Sequential. ACM SIGMOD Record. 47:1. (15-22). Online publication date: 10-Sep-2018.

    https://doi.org/10.1145/3277006.3277011

  • Ives Z. (2018). Technical Perspective:. ACM SIGMOD Record. 47:1. (14-14). Online publication date: 10-Sep-2018.

    https://doi.org/10.1145/3277006.3277010

  • Dhulipala L, Blelloch G and Shun J. Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures. (393-404).

    https://doi.org/10.1145/3210377.3210414

  • Deshpande A. In situ graph querying and analytics with graphgen. Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA). (1-2).

    https://doi.org/10.1145/3210259.3210261

  • Fan W, Lu P, Luo X, Xu J, Yin Q, Yu W and Xu R. Adaptive Asynchronous Parallelization of Graph Algorithms. Proceedings of the 2018 International Conference on Management of Data. (1141-1156).

    https://doi.org/10.1145/3183713.3196918

  • Liu S, Chen L, Li B and Carnegie A. A Hierarchical Synchronous Parallel Model for Wide-Area Graph Analytics. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. (531-539).

    https://doi.org/10.1109/INFOCOM.2018.8486361