KONECT

KONECT --- The Koblenz Network Collection. We present the Koblenz Network Collection (KONECT), a project to collect network datasets in the areas of web science, network science and related areas, as well as provide tools for their analysis. In the cited areas, a surprisingly large number of very heterogeneous data can be modeled as networks and consequently, a unified representation of networks can be used to gain insight into many kinds of problems. Due to the emergence of the World Wide Web in the last decades many such datasets are now openly available. The KONECT project thus has the goal of collecting many diverse network datasets from the Web, and providing a way for their systematic study. The main parts of KONECT are (1) a collection of over 160 network datasets, consisting of directed, undirected, unipartite, bipartite, weighted, unweighted, signed and temporal networks collected from the Web, (2) a Matlab toolbox for network analysis and (3) a website giving a compact overview the various computed statistics and plots. In this paper, we describe KONECT’s taxonomy of networks datasets, give an overview of the datasets included, review the supported statistics and plots, and briefly discuss KONECT’s role in the area of web science and network science


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  1. Afanasyev, I. V.; Voevodin, Vl. V.: Developing efficient implementations of connected component algorithms for NEC SX-Aurora TSUBASA (2020)
  2. Anwar, Raheel; Yousuf, Muhammad Irfan; Abid, Muhammad: Analysis of a model for generating weakly scale-free networks (2020)
  3. Balls-Barker, Bryn; Webb, Benjamin: Link prediction in networks using effective transitions (2020)
  4. Bloem, Peter; de Rooij, Steven: Large-scale network motif analysis using compression (2020)
  5. Bonald, Thomas; de Lara, Nathan; Lutz, Quentin; Charpentier, Bertrand: Scikit-network: graph analysis in Python (2020)
  6. Chu, Yi; Liu, Boxiao; Cai, Shaowei; Luo, Chuan; You, Haihang: An efficient local search algorithm for solving maximum edge weight clique problem in large graphs (2020)
  7. Garlet Millani, Marcelo; Molter, Hendrik; Niedermeier, Rolf; Sorge, Manuel: Efficient algorithms for measuring the funnel-likeness of DAGs (2020)
  8. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: a survey (2020)
  9. Li, Mingjie; Hao, Jin-Kao; Wu, Qinghua: General swap-based multiple neighborhood adaptive search for the maximum balanced biclique problem (2020)
  10. Safaei, Farshad; Babaei, Amin; Moudi, Mehrnaz: Optimally connected hybrid complex networks with windmill graphs backbone (2020)
  11. Wang, Tiandong; Resnick, Sidney I.: Degree growth rates and index estimation in a directed preferential attachment model (2020)
  12. Wan, Phyllis; Wang, Tiandong; Davis, Richard A.; Resnick, Sidney I.: Are extreme value estimation methods useful for network data? (2020)
  13. Xian, Jiajun; Yang, Dan; Pan, Liming; Wang, Wei: The optimal edge for containing the spreading of SIS model (2020)
  14. Yousuf, Muhammad Irfan; Kim, Suhyun: Generating graphs by creating associative and random links between existing nodes (2020)
  15. Chepoi, Victor; Dragan, Feodor F.; Habib, Michel; Vaxès, Yann; Alrasheed, Hend: Fast approximation of eccentricities and distances in hyperbolic graphs (2019)
  16. Giscard, Pierre-Louis; Kriege, Nils; Wilson, Richard C.: A general purpose algorithm for counting simple cycles and simple paths of any length (2019)
  17. Huang, Wei-Qiang; Lin, Wen-Wei; Lu, Henry Horng-Shing; Yau, Shing-Tung: iSIRA: integrated shift-invert residual Arnoldi method for graph Laplacian matrices from big data (2019)
  18. Lozano, Manuel; Trujillo, Humberto M.: Optimizing node infiltrations in complex networks by a local search based heuristic (2019)
  19. Saucan, Emil; Sreejith, R. P.; Vivek-Ananth, R. P.; Jost, Jürgen; Samal, Areejit: Discrete Ricci curvatures for directed networks (2019)
  20. Smith, Anna L.; Asta, Dena M.; Calder, Catherine A.: The geometry of continuous latent space models for network data (2019)

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