NetworKit: A tool suite for large-scale complex network analysis. NetworKit is a growing open-source toolkit for large-scale network analysis. Its aim is to provide tools for the analysis of large networks in the size range from thousands to billions of edges. For this purpose, it implements efficient graph algorithms, many of them parallel to utilize multicore architectures. These are meant to compute standard measures of network analysis, such as degree sequences, clustering coefficients, and centrality measures. In this respect, NetworKit is comparable to packages such as NetworkX, albeit with a focus on parallelism and scalability. NetworKit is also a testbed for algorithm engineering and contains novel algorithms from recently published research (see list of Publications). NetworKit is a Python module. Performance-aware algorithms are written in C++ (often using OpenMP for shared-memory parallelism) and exposed to Python via the Cython toolchain. Python in turn gives us the ability to work interactively and with a rich environment of tools for data analysis. Furthermore, NetworKit’s core can be built and used as a native library.

References in zbMATH (referenced in 13 articles )

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  1. Michail, Dimitrios; Kinable, Joris; Naveh, Barak; Sichi, John V.: JGraphT -- a Java library for graph data structures and algorithms (2020)
  2. Lozano, Manuel; Trujillo, Humberto M.: Optimizing node infiltrations in complex networks by a local search based heuristic (2019)
  3. Shaydulin, Ruslan; Chen, Jie; Safro, Ilya: Relaxation-based coarsening for multilevel hypergraph partitioning (2019)
  4. Carstens, Corrie Jacobien; Hamann, Michael; Meyer, Ulrich; Penschuck, Manuel; Tran, Hung; Wagner, Dorothea: Parallel and I/O-efficient randomisation of massive networks using global curveball trades (2018)
  5. van der Grinten, Alexander; Bergamini, Elisabetta; Green, Oded; Bader, David A.; Meyerhenke, Henning: Scalable Katz ranking computation in large static and dynamic graphs (2018)
  6. Hamann, Michael; Röhrs, Eike; Wagner, Dorothea: Local community detection based on small cliques (2017)
  7. Wegner, Michael; Taubert, Oskar; Schug, Alexander; Meyerhenke, Henning: Maxent-stress optimization of 3D biomolecular models (2017)
  8. Bergamini, Elisabetta; Meyerhenke, Henning: Approximating betweenness centrality in fully dynamic networks (2016)
  9. Hoske, Daniel; Lukarski, Dimitar; Meyerhenke, Henning; Wegner, Michael: Engineering a combinatorial Laplacian solver: lessons learned (2016)
  10. Riondato, Matteo; Kornaropoulos, Evgenios M.: Fast approximation of betweenness centrality through sampling (2016)
  11. von Looz, Moritz; Meyerhenke, Henning: Querying probabilistic neighborhoods in spatial data sets efficiently (2016)
  12. Bergamini, Elisabetta; Meyerhenke, Henning: Fully-dynamic approximation of betweenness centrality (2015)
  13. Brandes, Ulrik; Hamann, Michael; Strasser, Ben; Wagner, Dorothea: Fast quasi-threshold editing (2015)