SNAP

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network. The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation. SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.


References in zbMATH (referenced in 166 articles )

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  1. Li, Lingjie; Yu, Wenjian; Batselier, Kim: Faster tensor train decomposition for sparse data (2022)
  2. Zhang, Yuan; Xia, Dong: Edgeworth expansions for network moments (2022)
  3. Zhou, Yi; Lin, Weibo; Hao, Jin-Kao; Xiao, Mingyu; Jin, Yan: An effective branch-and-bound algorithm for the maximum (s)-bundle problem (2022)
  4. Athreya, Siva; den Hollander, Frank; Röllin, Adrian: Graphon-valued stochastic processes from population genetics (2021)
  5. Chen, Xiaoyu; Zhou, Yi; Hao, Jin-Kao; Xiao, Mingyu: Computing maximum (k)-defective cliques in massive graphs (2021)
  6. Cruciani, Emilio; Natale, Emanuele; Nusser, André; Scornavacca, Giacomo: Phase transition of the 2-choices dynamics on core-periphery networks (2021)
  7. Di Battista, Giuseppe; Frati, Fabrizio; Patrignani, Maurizio; Tais, Marco: Schematic representation of large biconnected graphs (2021)
  8. Di Benedetto, Giuseppe; Caron, François; Teh, Yee Whye: Nonexchangeable random partition models for microclustering (2021)
  9. Dziubiński, Marcin; Goyal, Sanjeev; Minarsch, David E. N.: The strategy of conquest (2021)
  10. Enright, Jessica; Meeks, Kitty; Mertzios, George B.; Zamaraev, Viktor: Deleting edges to restrict the size of an epidemic in temporal networks (2021)
  11. Ghaedsharaf, Yaser; Siami, Milad; Somarakis, Christoforos; Motee, Nader: Centrality in time-delay consensus networks with structured uncertainties (2021)
  12. Ha, Wooseok; Fountoulakis, Kimon; Mahoney, Michael W.: Statistical guarantees for local graph clustering (2021)
  13. Hu, Qian-Ying; Wen, Chun; Huang, Ting-Zhu; Shen, Zhao-Li; Gu, Xian-Ming: A variant of the Power-Arnoldi algorithm for computing PageRank (2021)
  14. Keikha, Vahideh; Aghamolaei, Sepideh; Mohades, Ali; Ghodsi, Mohammad: Clustering geometrically-modeled points in the aggregated uncertainty model (2021)
  15. Kumar, Pawan; Dohare, Ravins: Formalising and detecting community structures in real world complex networks (2021)
  16. Liang, Ziwei; Yuan, He; Du, Hongwei: Two-stage pricing strategy with price discount in online social networks (2021)
  17. Lu, Juan; Gong, Zhiguo; Yang, Yiyang: A matrix sampling approach for efficient SimRank computation (2021)
  18. Matthew Treinish, Ivan Carvalho, Georgios Tsilimigkounakis, Nahum Sá: retworkx: A High-Performance Graph Library for Python (2021) arXiv
  19. Tuck, Jonathan; Barratt, Shane; Boyd, Stephen: A distributed method for fitting Laplacian regularized stratified models (2021)
  20. Wu, Hong; Zhang, Zhijian; Fang, Yabo; Zhang, Shaotang; Jiang, Zuo; Huang, Jian; Li, Ping: Containment of rumor spread by selecting immune nodes in social networks (2021)

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Further publications can be found at: http://snap.stanford.edu/papers.html