References#

Architecture#

2-D Data Partitioning

  • Kang, S., Fender, A., Eaton, J., & Rees, B. (2020, September) Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE.

  • S. Kang, J. Nke and B. Rees, (2022 September) Analyzing Multi-trillion Edge Graphs on Large GPU Clusters: A Case Study with PageRank, In 2022 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, 2022, pp. 1-7, doi: 10.1109/HPEC55821.2022.9926341.



Algorithms#

Betweenness Centrality#

  • Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of mathematical sociology, 25(2), 163-177.

  • Brandes, U. (2008). On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30(2), 136-145.

  • McLaughlin, A., & Bader, D. A. (2018). Accelerating GPU betweenness centrality. Communications of the ACM, 61(8), 85-92.

Katz#

  • Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18(1), 39-43.

  • Foster, K.C., Muth, S.Q., Potterat, J.J. et al. A faster Katz status score algorithm. Computational & Mathematical Organization Theory (2001) 7: 275.

K-Truss#

  • J. Cohen, Trusses: Cohesive subgraphs for social network analysis National security agency technical report, 2008

  • O. Green, J. Fox, E. Kim, F. Busato, et al. Quickly Finding a Truss in a Haystack IEEE High Performance Extreme Computing Conference (HPEC), 2017 https://doi.org/10.1109/HPEC.2017.8091038

  • O. Green, P. Yalamanchili, L.M. Munguia, Fast Triangle Counting on GPU Irregular Applications: Architectures and Algorithms (IA3), 2014

Hungarian Algorithm#

  • Date, K., & Nagi, R. (2016). GPU-accelerated Hungarian algorithms for the Linear Assignment Problem. Parallel Computing, 57, 52-72.

Leiden#

  • Traag, V. A., Waltman, L., & Van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 1-12.

Louvain#

  • VD Blondel, J-L Guillaume, R Lambiotte and E Lefebvre. Fast unfolding of community hierarchies in large networks. J Stat Mech P10008 (2008)



Other Papers#

  • Hricik, T., Bader, D., & Green, O. (2020, September). Using RAPIDS AI to Accelerate Graph Data Science Workflows. In 2020 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-4). IEEE.

  • Tang, Jiawei & Gao, Min & Xiao, Yu & Li, Cong & Chen, Yang. (2024). EGGPU: Enabling Efficient Large-Scale Network Analysis with Consumer-Grade GPUs. 10.1145/3698387.3699997. https://www.researchgate.net/publication/384925237_EGGPU_Enabling_Efficient_Large-Scale_Network_Analysis_with_Consumer-Grade_GPUs

  • N. Keskes1. GPU Acceleration of Graph Algorithms in NextVision: A Seismic Data Interpretation Tool Eighth EAGE High Performance Computing Workshop, Sep 2024, Volume 2024, p.1 - 3 https://www.earthdoc.org/content/papers/10.3997/2214-4609.2024636024