DANCer: dynamic attributed networks with community structure generation

Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one community to another. The aim of community detection methods is precisely to detect the evolution of these communities. However, evaluating these algorithms requires tests on real or artificial networks with verifiable ground truth. Dynamic networks generators have been recently proposed for this task, but most of them consider only the structure of the network, disregarding the characteristics of the nodes. In this paper, we propose a new generator for dynamic attributed networks with community structure that follow the properties of real-world networks. The evolution of the network is performed using two kinds of operations: Micro-operations are applied on the edges and vertices, while macro-operations on the communities. Moreover, the properties of real-world networks such as preferential attachment or homophily are preserved during the evolution of the network, as confirmed by our experiments.

[1]  Ling Huang,et al.  Evolution of social-attribute networks: measurements, modeling, and implications using google+ , 2012, Internet Measurement Conference.

[2]  Dorothea Wagner,et al.  An Efficient Generator for Clustered Dynamic Random Networks , 2012, MedAlg.

[3]  Chris Arney,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Easley, D. and Kleinberg, J.; 2010) [Book Review] , 2013, IEEE Technology and Society Magazine.

[4]  Christos Faloutsos,et al.  Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication , 2005, PKDD.

[5]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[6]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[7]  F. Radicchi,et al.  Benchmark graphs for testing community detection algorithms. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[9]  Carlos Riquelme,et al.  Learning multifractal structure in large networks , 2014, KDD.

[10]  P. Holland,et al.  TRANSITIVITY IN STRUCTURAL MODELS OF SMALL GROUPS , 1977 .

[11]  Christos Faloutsos,et al.  RTM: Laws and a Recursive Generator for Weighted Time-Evolving Graphs , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[12]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[13]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[15]  László Lovász,et al.  Multifractal network generator , 2010, Proceedings of the National Academy of Sciences.

[16]  A. Rbnyi.  ON THE EVOLUTION OF RANDOM GRAPHS , 2001 .

[17]  F. Chung,et al.  The average distances in random graphs with given expected degrees , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Jure Leskovec,et al.  Multiplicative Attribute Graph Model of Real-World Networks , 2010, Internet Math..

[19]  Jennifer Neville,et al.  Attributed graph models: modeling network structure with correlated attributes , 2014, WWW.

[20]  Andrea Lancichinetti,et al.  Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[22]  Christos Faloutsos,et al.  Kronecker Graphs: An Approach to Modeling Networks , 2008, J. Mach. Learn. Res..

[23]  P. Lazarsfeld,et al.  Friendship as Social process: a substantive and methodological analysis , 1964 .

[24]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[25]  Garry Robins,et al.  A spatial model for social networks , 2006 .

[26]  Santo Fortunato,et al.  A benchmark model to assess community structure in evolving networks , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Osmar R. Zaïane,et al.  Generating Attributed Networks with Communities , 2015, PloS one.

[28]  Christos Faloutsos,et al.  RTG: a recursive realistic graph generator using random typing , 2009, Data Mining and Knowledge Discovery.

[29]  B. Bollobás.  The evolution of random graphs , 1984 .

[30]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[32]  Jure Leskovec,et al.  Microscopic evolution of social networks , 2008, KDD.

[33]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[34]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.