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 42 articles )

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  1. Dziubiński, Marcin; Goyal, Sanjeev; Minarsch, David E. N.: The strategy of conquest (2021)
  2. 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)
  3. Abhyankar, Shrirang; Betrie, Getnet; Maldonado, Daniel Adrian; Mcinnes, Lois C.; Smith, Barry; Zhang, Hong: PETSc DMNetwork: a library for scalable network PDE-based multiphysics simulations (2020)
  4. Afanasyev, I. V.; Voevodin, Vl. V.: Developing efficient implementations of connected component algorithms for NEC SX-Aurora TSUBASA (2020)
  5. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  6. Jisung Yoon, Kai-Cheng Yang, Woo-Sung Jung, Yong-Yeol Ahn: Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs (2020) arXiv
  7. Michail, Dimitrios; Kinable, Joris; Naveh, Barak; Sichi, John V.: JGraphT -- a Java library for graph data structures and algorithms (2020)
  8. Siddhartha Sahu, Semih Salihoglu: Graphsurge: Graph Analytics on View Collections Using Differential Computation (2020) arXiv
  9. Wang, Tiandong; Resnick, Sidney I.: Degree growth rates and index estimation in a directed preferential attachment model (2020)
  10. Xu Dong, Luis Castro, Nazrul Shaikh: fastnet: An R Package for Fast Simulation and Analysis of Large-Scale Social Networks (2020) not zbMATH
  11. Davis, Timothy A.: Algorithm 1000: SuiteSparse:GraphBLAS: graph algorithms in the language of sparse linear algebra (2019)
  12. Dimitrios Michail, Joris Kinable, Barak Naveh, John V Sichi: JGraphT - A Java library for graph data structures and algorithms (2019) arXiv
  13. Hu, Xiaozhe; Lin, Junyuan; Zikatanov, Ludmil T.: An adaptive multigrid method based on path cover (2019)
  14. Fasi, Massimiliano; Iannazzo, Bruno: Computing the weighted geometric mean of two large-scale matrices and its inverse times a vector (2018)
  15. Fosdick, Bailey K.; Larremore, Daniel B.; Nishimura, Joel; Ugander, Johan: Configuring random graph models with fixed degree sequences (2018)
  16. van der Grinten, Alexander; Bergamini, Elisabetta; Green, Oded; Bader, David A.; Meyerhenke, Henning: Scalable Katz ranking computation in large static and dynamic graphs (2018)
  17. van der Hofstad, Remco; van Leeuwaarden, Johan S. H.; Stegehuis, Clara: Triadic closure in configuration models with unbounded degree fluctuations (2018)
  18. Azadbakht, Keyvan; Bezirgiannis, Nikolaos; de Boer, Frank S.: Distributed network generation based on preferential attachment in ABS (2017)
  19. Cui, Huanqing; Niu, Jian; Zhou, Chuanai; Shu, Minglei: A multi-threading algorithm to detect and remove cycles in vertex- and arc-weighted digraph (2017)
  20. Fox, Alyson; Manteuffel, Thomas; Sanders, Geoffrey: Numerical methods for Gremban’s expansion of signed graphs (2017)

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