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 13 articles )

Showing results 1 to 13 of 13.
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  1. Meyerhenke, Henning; Sanders, Peter; Schulz, Christian: Partitioning (hierarchically clustered) complex networks via size-constrained graph clustering (2016)
  2. Prokhorenkova, L.A.; Krot, A.V.: Local clustering coefficients in preferential attachment models (2016)
  3. Zhou, Yunkai; Wang, Zheng; Zhou, Aihui: Accelerating large partial EVD/SVD calculations by filtered block Davidson methods (2016)
  4. Borassi, Michele; Crescenzi, Pierluigi; Habib, Michel; Kosters, Walter A.; Marino, Andrea; Takes, Frank W.: Fast diameter and radius BFS-based computation in (weakly connected) real-world graphs (2015)
  5. David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosic, Stephen Boyd, Jure Leskovec: SnapVX: A Network-Based Convex Optimization Solver (2015) arXiv
  6. Ye, Bin; Qiu, Liang; Wang, Xuesong; Guhr, Thomas: Spectral statistics in directed complex networks and universality of the Ginibre ensemble (2015)
  7. Crescenzi, Pilu; Grossi, Roberto; Habib, Michel; Lanzi, Leonardo; Marino, Andrea: On computing the diameter of real-world undirected graphs (2013)
  8. Nettleton, David F.: Data mining of social networks represented as graphs (2013)
  9. Chen, Jie; Safro, Ilya: Algebraic distance on graphs (2011)
  10. Safro, Ilya; Temkin, Boris: Multiscale approach for the network compression-friendly ordering (2011)
  11. Kim, Myunghwan; Leskovec, Jure: Multiplicative attribute graph model of real-world networks (2010)
  12. Leskovec, Jure; Chakrabarti, Deepayan; Kleinberg, Jon; Faloutsos, Christos; Ghahramani, Zoubin: Kronecker graphs: an approach to modeling networks (2010)
  13. Leskovec, Jure; Lang, Kevin J.; Dasgupta, Anirban; Mahoney, Michael W.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters (2009)

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