Selection Strategy of Nodes with the Greatest Influence on Community Structure

In complex networks, it is always a hot issue to assess the importance or influence of a node in the whole network. As a special network structure, community is widely used in Delay Tolerant Networks (DTN) and worm containment of Online Social Networks (OSN). The change of community structure has a great impact on the performance of community based routing or worm control. This paper is based on the community structure, which nodes are considered to have the greatest influence on the community structure in the network, the local influence and global influence of the node in the community structure are studied. We present a method to assess the influence of nodes on the network community structure. Firstly, the sub-modules are identified that have the greatest influence on the structure of the community from all the communities, then the sub-modules are assessed, and finally most influential nodes are selected from these sub-modules. The experimental results show that compared with the traditional methods for assessing node's influence, our method can identify the node which has the greatest influence on the community structure.

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

[2]  Xiang Li,et al.  Structural Vulnerability Assessment of Community-Based Routing in Opportunistic Networks , 2016, IEEE Transactions on Mobile Computing.

[3]  Duanbing Chen,et al.  Detecting overlapping communities of weighted networks via a local algorithm , 2010 .

[4]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.

[5]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[6]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[7]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[8]  P. Bonacich.  Factoring and weighting approaches to status scores and clique identification , 1972 .

[9]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

[10]  Wu Xin.  Influence Analysis of Online Social Networks , 2014 .

[11]  Ming Tang,et al.  Improving the accuracy of the k-shell method by removing redundant links: From a perspective of spreading dynamics , 2015, Scientific Reports.

[12]  Nam P. Nguyen,et al.  Assessing network vulnerability in a community structure point of view , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[13]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[14]  R. Burt,et al.  Social network analysis: foundations and frontiers on advantage. , 2013, Annual review of psychology.

[15]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[16]  Guanrong Chen,et al.  Behaviors of susceptible-infected epidemics on scale-free networks with identical infectivity. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[18]  Mark S. Granovetter.  Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[19]  An Zeng,et al.  Predicting the evolution of spreading on complex networks , 2014, Scientific Reports.

[20]  Liu Zhi,et al.  Evaluating influential spreaders in complex networks by extension of degree , 2015 .

[21]  Gert Sabidussi,et al.  The centrality index of a graph , 1966 .

[22]  Duncan J Watts,et al.  A simple model of global cascades on random networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Andrea Lancichinetti,et al.  Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.

[24]  Steve Gregory,et al.  Finding overlapping communities in networks by label propagation , 2009, ArXiv.

[25]  Christos Faloutsos,et al.  Patterns of Cascading Behavior in Large Blog Graphs , 2007, SDM.

[26]  Yi-Cheng Zhang,et al.  Leaders in Social Networks, the Delicious Case , 2011, PloS one.

[27]  Phillip Bonacich,et al.  Some unique properties of eigenvector centrality , 2007, Soc. Networks.