GraphScope

GraphScope: Parameter-free Mining of Large Time-evolving Graphs. How can we find communities in dynamic networks of socialinteractions, such as who calls whom, who emails whom, or who sells to whom? How can we spot discontinuity time-points in such streams of graphs, in an on-line, any-time fashion? We propose GraphScope, that addresses both problems, using information theoretic principles. Contrary to the majority of earlier methods, it needs no user-defined parameters. Moreover, it is designed to operate on large graphs, in a streaming fashion. We demonstrate the efficiency and effectiveness of our GraphScope on real datasets from several diverse domains. In all cases it produces meaningful time-evolving patterns that agree with human intuition.


References in zbMATH (referenced in 23 articles )

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  1. Bhamidi, Shankar; Jin, Jimmy; Nobel, Andrew: Change point detection in network models: preferential attachment and long range dependence (2018)
  2. Boullé, Marc: Hierarchical two-part MDL code for multinomial distributions (2018)
  3. Guigourès, Romain; Boullé, Marc; Rossi, Fabrice: Discovering patterns in time-varying graphs: a triclustering approach (2018)
  4. Kostakis, Orestis; Tatti, Nikolaj; Gionis, Aristides: Discovering recurring activity in temporal networks (2017)
  5. Pignolet, Yvonne Anne; Roy, Matthieu; Schmid, Stefan; Tredan, Gilles: The many faces of graph dynamics (2017)
  6. Wang, Peizhuo; Gao, Lin; Ma, Xiaoke: Dynamic community detection based on network structural perturbation and topological similarity (2017)
  7. Uno, Takeaki; Uno, Yushi: Mining preserving structures in a graph sequence (2016)
  8. Eustace, Justine; Wang, Xingyuan; Cui, Yaozu: Community detection using local neighborhood in complex networks (2015)
  9. Zhang, Wenlu; Li, Rongjian; Feng, Daming; Chernikov, Andrey; Chrisochoides, Nikos; Osgood, Christopher; Ji, Shuiwang: Evolutionary soft co-clustering: formulations, algorithms, and applications (2015)
  10. Aggarwal, Charu; Subbian, Karthik: Evolutionary network analysis: a survey (2014)
  11. Wang, Li; Wang, Jiang; Bi, Yuanjun; Wu, Weili; Xu, Wen; Lian, Biao: Noise-tolerance community detection and evolution in dynamic social networks (2014)
  12. Wu, Zhenyu; Zou, Ming: An incremental community detection method for social tagging systems using locality-sensitive hashing (2014) ioport
  13. Xu, Kevin S.; Kliger, Mark; Hero, Alfred O. III: Adaptive evolutionary clustering (2014)
  14. Langone, Rocco; Alzate, Carlos; Suykens, Johan A. K.: Kernel spectral clustering with memory effect (2013)
  15. Malliaros, Fragkiskos D.; Vazirgiannis, Michalis: Clustering and community detection in directed networks: a survey (2013)
  16. Sim, Kelvin; Gopalkrishnan, Vivekanand; Zimek, Arthur; Cong, Gao: A survey on enhanced subspace clustering (2013)
  17. Cheng, Hong; Zhou, Yang; Huang, Xin; Yu, Jeffrey Xu: Clustering large attributed information networks: an efficient incremental computing approach (2012)
  18. Papadopoulos, Symeon; Kompatsiaris, Yiannis; Vakali, Athena; Spyridonos, Ploutarchos: Community detection in social media performance and application considerations (2012) ioport
  19. Plant, Claudia; Thai, Son Mai; Shao, Junming; Theis, Fabian J.; Meyer-Baese, Anke; Böhm, Christian: Measuring non-Gaussianity by Phi-transformed and fuzzy histograms (2012) ioport
  20. Saganowski, Stanisław; Bródka, Piotr; Kazienko, Przemysław: Influence of the user importance measure on the group evolution discovery (2012)

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