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.


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

    1. Green, J. Fox, E. Kim, F. Busato, et al. Quickly Finding a Truss in a Haystack IEEE High Performance Extreme Computing Conference (HPEC), 2017

    1. Green, P. Yalamanchili, L.M. Munguia, “ast 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.

Data Sets

      1. Zachary, An information flow model for conflict and fission in small groups, Journal of Anthropological Research 33, 452-473 (1977).

  • D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations, Behavioral Ecology and Sociobiology 54, 396-405 (2003).

  • M. E. J. Newman, Finding community structure in networks using the eigenvectors of matrices, Preprint physics/0605087 (2006).

  • Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. Local Higher-order Graph Clustering. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017.

  • J. Leskovec, J. Kleinberg and C. Faloutsos. Graph Evolution: Densification and Shrinking Diameters. ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 1(1), 2007.