Mining Essential Relationships under Multiplex Networks

In big data times, massive datasets often carry different relationships among the same group of nodes, analyzing on these heterogeneous relationships may give us a window to peek the essential relationships among nodes. In this paper, first of all we propose a new metric “similarity rate” in order to capture the changing rate of similarities between node-pairs though all networks; secondly, we try to use this new metric to uncover essential relationships between node-pairs which essential relationships are often hidden and hard to get. From experiments study of Indonesian Terrorists dataset, this new metric similarity rate function well for giving us a way to uncover essential relationships from lots of appearances. Keywords—big data; essential relationships; multiplex networks; similarity rate; group detection

[1]  Sune Lehmann,et al.  Link communities reveal multiscale complexity in networks , 2009, Nature.

[2]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Matteo Magnani,et al.  Pareto Distance for Multi-layer Network Analysis , 2013, SBP.

[4]  Sergio Gómez,et al.  Multiple resolution of the modular structure of complex networks , 2007, ArXiv.

[5]  Katarzyna Musial,et al.  Multidimensional Social Network: Model and Analysis , 2011, ICCCI.

[6]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[7]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Vincenza Carchiolo,et al.  Communities Unfolding in Multislice Networks , 2016, CompleNet.

[9]  Linyuan Lü,et al.  Predicting missing links via local information , 2009, 0901.0553.

[10]  Huan Liu,et al.  Community Detection in Multi-Dimensional Networks , 2015 .

[11]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[12]  Kan Li,et al.  A Unified Model for Community Detection of Multiplex Networks , 2014, WISE.

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

[14]  Sergio Gómez,et al.  Size reduction of complex networks preserving modularity , 2007, ArXiv.

[15]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[16]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[17]  Barbora Micenková,et al.  Combinatorial Analysis of Multiple Networks , 2013, ArXiv.

[18]  Jukka-Pekka Onnela,et al.  Community Structure in Time-Dependent, Multiscale, and Multiplex Networks , 2009, Science.

[19]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.