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  • Agarwal S, Dutta S and Bhattacharya A. (2020). ChiSeL. Proceedings of the VLDB Endowment. 13:10. (1654-1668). Online publication date: 1-Jun-2020.

    https://doi.org/10.14778/3401960.3401964

  • Lakhotia K, Kannan R, Pati S and Prasanna V. (2020). GPOP. ACM Transactions on Parallel Computing. 7:1. (1-24). Online publication date: 2-Apr-2020.

    https://doi.org/10.1145/3380942

  • Kolokasis I and Pratikakis P. Cut to Fit. Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA). (1-10).

    https://doi.org/10.1145/3327964.3328498

  • Pacaci A and Özsu M. Experimental Analysis of Streaming Algorithms for Graph Partitioning. Proceedings of the 2019 International Conference on Management of Data. (1375-1392).

    https://doi.org/10.1145/3299869.3300076

  • Heidari S, Simmhan Y, Calheiros R and Buyya R. (2018). Scalable Graph Processing Frameworks. ACM Computing Surveys. 51:3. (1-53). Online publication date: 31-May-2019.

    https://doi.org/10.1145/3199523

  • Wu Y, Cai W, Li Z, Tan W and Hou X. (2019). Efficient Parallel Simulation over Large-scale Social Contact Networks. ACM Transactions on Modeling and Computer Simulation. 29:2. (1-25). Online publication date: 25-Apr-2019.

    https://doi.org/10.1145/3265749

  • Chen R, Shi J, Chen Y, Zang B, Guan H and Chen H. (2019). PowerLyra. ACM Transactions on Parallel Computing. 5:3. (1-39). Online publication date: 23-Jan-2019.

    https://doi.org/10.1145/3298989

  • Dathathri R, Gill G, Hoang L, Dang H, Brooks A, Dryden N, Snir M and Pingali K. (2018). Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics. ACM SIGPLAN Notices. 53:4. (752-768). Online publication date: 2-Dec-2018.

    https://doi.org/10.1145/3296979.3192404

  • Gill G, Dathathri R, Hoang L and Pingali K. (2018). A study of partitioning policies for graph analytics on large-scale distributed platforms. Proceedings of the VLDB Endowment. 12:4. (321-334). Online publication date: 1-Dec-2018.

    https://doi.org/10.14778/3297753.3297754

  • Moreira O, Popp M and Schulz C. Evolutionary multi-level acyclic graph partitioning. Proceedings of the Genetic and Evolutionary Computation Conference. (332-339).

    https://doi.org/10.1145/3205455.3205464

  • Dathathri R, Gill G, Hoang L, Dang H, Brooks A, Dryden N, Snir M and Pingali K. Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics. Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation. (752-768).

    https://doi.org/10.1145/3192366.3192404

  • Ding Y, Kondor R and Eskreis-Winkler J. Multiresolution kernel approximation for Gaussian process regression. Proceedings of the 31st International Conference on Neural Information Processing Systems. (3743-3751).

    /doi/10.5555/3294996.3295131

  • Wu Y, Hou X, Tan W, Li Z and Cai W. Efficient Parallel Simulation over Social Contact Network with Skewed Degree Distribution. Proceedings of the 2017 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. (65-75).

    https://doi.org/10.1145/3064911.3064934

  • Lim Y, Lee W, Choi H and Kang U. (2017). MTP. World Wide Web. 20:3. (491-514). Online publication date: 1-May-2017.

    https://doi.org/10.1007/s11280-016-0393-1

  • Jiao L, Li J, Xu T, Du W and Fu X. (2016). Optimizing cost for online social networks on geo-distributed clouds. IEEE/ACM Transactions on Networking. 24:1. (99-112). Online publication date: 1-Feb-2016.

    https://doi.org/10.1109/TNET.2014.2359365

  • McCune R, Weninger T and Madey G. (2015). Thinking Like a Vertex. ACM Computing Surveys. 48:2. (1-39). Online publication date: 21-Nov-2015.

    https://doi.org/10.1145/2818185

  • Zhang Y, Liu Y, Yu J, Liu P and Guo L. VSEP. Proceedings of the ICA3PP International Workshops and Symposiums on Algorithms and Architectures for Parallel Processing - Volume 9532. (71-84).

    https://doi.org/10.1007/978-3-319-27161-3_7

  • Kang Y, Gu X, Wang W and Meng D. Scalable Clustering Algorithm via a Triangle Folding Processing for Complex Networks. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. (33-42).

    https://doi.org/10.1145/2806416.2806563

  • Rahimian F, Payberah A, Girdzijauskas S, Jelasity M and Haridi S. (2015). A Distributed Algorithm for Large-Scale Graph Partitioning. ACM Transactions on Autonomous and Adaptive Systems. 10:2. (1-24). Online publication date: 9-Jun-2015.

    https://doi.org/10.1145/2714568

  • Chen R, Shi J, Chen Y and Chen H. PowerLyra. Proceedings of the Tenth European Conference on Computer Systems. (1-15).

    https://doi.org/10.1145/2741948.2741970

  • Shen Y, Chen G, Jagadish H, Lu W, Ooi B and Tudor B. (2014). Fast failure recovery in distributed graph processing systems. Proceedings of the VLDB Endowment. 8:4. (437-448). Online publication date: 1-Dec-2014.

    https://doi.org/10.14778/2735496.2735506

  • Yuan P, Zhang W, Xie C, Jin H, Liu L and Lee K. Fast iterative graph computation. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. (401-412).

    https://doi.org/10.1109/SC.2014.38

  • Dai D, Ross R, Carns P, Kimpe D and Chen Y. Using property graphs for rich metadata management in HPC systems. Proceedings of the 9th Parallel Data Storage Workshop. (7-12).

    https://doi.org/10.1109/PDSW.2014.11

  • Nilizadeh S, Kapadia A and Ahn Y. Community-Enhanced De-anonymization of Online Social Networks. Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. (537-548).

    https://doi.org/10.1145/2660267.2660324

  • Bourse F, Lelarge M and Vojnovic M. Balanced graph edge partition. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. (1456-1465).

    https://doi.org/10.1145/2623330.2623660

  • Meyerhenke H, Sanders P and Schulz C. Partitioning Complex Networks via Size-Constrained Clustering. Proceedings of the 13th International Symposium on Experimental Algorithms - Volume 8504. (351-363).

    https://doi.org/10.1007/978-3-319-07959-2_30

  • Rahimian F, Payberah A, Girdzijauskas S and Haridi S. Distributed Vertex-Cut Partitioning. Proceedings of the 14th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems - Volume 8460. (186-200).

    https://doi.org/10.1007/978-3-662-43352-2_15

  • Boman E, Devine K and Rajamanickam S. Scalable matrix computations on large scale-free graphs using 2D graph partitioning. Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. (1-12).

    https://doi.org/10.1145/2503210.2503293

  • Hoque I and Gupta I. LFGraph. Proceedings of the First ACM SIGOPS Conference on Timely Results in Operating Systems. (1-17).

    https://doi.org/10.1145/2524211.2524218

  • Sarwat M, Elnikety S, He Y and Mokbel M. (2013). Horton+. Proceedings of the VLDB Endowment. 6:14. (1918-1929). Online publication date: 1-Sep-2013.

    https://doi.org/10.14778/2556549.2556573

  • Jain N, Liao G and Willke T. GraphBuilder. First International Workshop on Graph Data Management Experiences and Systems. (1-6).

    https://doi.org/10.1145/2484425.2484429

  • Xin R, Gonzalez J, Franklin M and Stoica I. GraphX. First International Workshop on Graph Data Management Experiences and Systems. (1-6).

    https://doi.org/10.1145/2484425.2484427

  • Chu S and Cheng J. (2012). Triangle listing in massive networks. ACM Transactions on Knowledge Discovery from Data. 6:4. (1-32). Online publication date: 1-Dec-2012.

    https://doi.org/10.1145/2382577.2382581

  • You G, Hwang S, Song Y, Jiang L and Nie Z. (2012). Efficient Entity Translation Mining. ACM Transactions on Information Systems. 30:4. (1-23). Online publication date: 1-Nov-2012.

    https://doi.org/10.1145/2382438.2382444

  • Yang Y, Yu J, Gao H and Li J. Finding the optimal path over multi-cost graphs. Proceedings of the 21st ACM international conference on Information and knowledge management. (2124-2128).

    https://doi.org/10.1145/2396761.2398586

  • Gonzalez J, Low Y, Gu H, Bickson D and Guestrin C. PowerGraph. Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation. (17-30).

    /doi/10.5555/2387880.2387883

  • Zeng Z, Wu B and Wang H. A parallel graph partitioning algorithm to speed up the large-scale distributed graph mining. Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications. (61-68).

    https://doi.org/10.1145/2351316.2351325

  • Xu T, Zhang Z, Yu P and Long B. (2012). Generative Models for Evolutionary Clustering. ACM Transactions on Knowledge Discovery from Data. 6:2. (1-27). Online publication date: 1-Jul-2012.

    https://doi.org/10.1145/2297456.2297459

  • Song H, Savas B, Cho T, Dave V, Lu Z, Dhillon I, Zhang Y and Qiu L. Clustered embedding of massive social networks. Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems. (331-342).

    https://doi.org/10.1145/2254756.2254796

  • Song H, Savas B, Cho T, Dave V, Lu Z, Dhillon I, Zhang Y and Qiu L. (2012). Clustered embedding of massive social networks. ACM SIGMETRICS Performance Evaluation Review. 40:1. (331-342). Online publication date: 7-Jun-2012.

    https://doi.org/10.1145/2318857.2254796

  • Osipov V, Sanders P and Schulz C. Engineering graph partitioning algorithms. Proceedings of the 11th international conference on Experimental Algorithms. (18-26).

    https://doi.org/10.1007/978-3-642-30850-5_3

  • Jiao L, Xu T, Li J and Fu X. Latency-aware data partitioning for geo-replicated online social networks. Proceedings of the Workshop on Posters and Demos Track. (1-2).

    https://doi.org/10.1145/2088960.2088975

  • Yoo A, Baker A, Pearce R and Van Emden Henson . A scalable eigensolver for large scale-free graphs using 2D graph partitioning. Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis. (1-11).

    https://doi.org/10.1145/2063384.2063469

  • Chu S and Cheng J. Triangle listing in massive networks and its applications. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. (672-680).

    https://doi.org/10.1145/2020408.2020513

  • Evans M, Yang K, Kang J and Shekhar S. A Lagrangian approach for storage of spatio-temporal network datasets. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. (212-221).

    https://doi.org/10.1145/1869790.1869822

  • Macropol K and Singh A. (2010). Scalable discovery of best clusters on large graphs. Proceedings of the VLDB Endowment. 3:1-2. (693-702). Online publication date: 1-Sep-2010.

    https://doi.org/10.14778/1920841.1920930

  • Zhang C and Wang F. (2010). A multilevel approach for learning from labeled and unlabeled data on graphs. Pattern Recognition. 43:6. (2301-2314). Online publication date: 1-Jun-2010.

    https://doi.org/10.1016/j.patcog.2009.12.025

  • Kaushik D, Smith M, Wollaber A, Smith B, Siegel A and Yang W. Enabling high-fidelity neutron transport simulations on petascale architectures. Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis. (1-12).

    https://doi.org/10.1145/1654059.1654128

  • Jiang X, Xiong H, Wang C and Tan A. (2009). Mining globally distributed frequent subgraphs in a single labeled graph. Data & Knowledge Engineering. 68:10. (1034-1058). Online publication date: 1-Oct-2009.

    https://doi.org/10.1016/j.datak.2009.04.008

  • Oldenburg S, Garbe M and Cap C. Similarity cross-analysis of tag / co-tag spaces in social classification systems. Proceedings of the 2008 ACM workshop on Search in social media. (11-18).

    https://doi.org/10.1145/1458583.1458587

  • Chrisochoides N, Fedorov A, Kot A, Archip N, Black P, Clatz O, Golby A, Kikinis R and Warfield S. Toward real-time image guided neurosurgery using distributed and grid computing. Proceedings of the 2006 ACM/IEEE conference on Supercomputing. (76-es).

    https://doi.org/10.1145/1188455.1188536