Tip Over Community: Backbone Nodes Detection Based on Community Structure

Key nodes detection is conducive to the information promotion and public opinion monitoring. Traditional works mainly focus on the nodes affecting the breadth of information diffusion. However, there are several works concentrate on the depth of information diffusion. Breadth dictates the number of nodes receiving information in one-hop propagation. Depth implies the length of the retweeting cascade of information and the number of social circles getting information. Depth is as important as breadth, for the nodes which contribute to the depth of information diffusion can promote society propagation (SP). If we have a good control of these nodes, information will ignite the community and minority will tip over the cognitive of the group. The purpose of this paper is to identify the influential nodes which can influence the SP in breadth and depth. With the analysis of SP, we proposed backbone node detection based on community structure (BNDCS) to detect these nodes. In this paper, we defined backbone nodes (BNs) by the location feature and the process of information diffusion. Besides, we presented a cohesion subgroup detection method based on hybrid seed expansion. It can adapt the network with no significant community structure and fit the star topology well. We proposed an information tension factor to measure the influence of bridge nodes. Through the experiment contrasts, the BNDCS is superior to compared methods. It showed that the BNs play a critical role in SP. Therefore, monitoring the BNs can prevent the mass incidents occurring and stop the spread of panic.

[1]  Xin Jin,et al.  Research on Social Network Structure and Public Opinions Dissemination of Micro-blog Based on Complex Network Analysis , 2013, J. Networks.

[2]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

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

[4]  L. Jiao,et al.  Multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks , 2017 .

[5]  Yu Xue,et al.  A community integration strategy based on an improved modularity density increment for large-scale networks , 2017 .

[6]  Tony White,et al.  Modelling Influence in a Social Network: Metrics and Evaluation , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[7]  Zhi-Dan Zhao,et al.  Identifying a set of influential spreaders in complex networks , 2016, Scientific Reports.

[8]  Ming Tang,et al.  Core-like groups result in invalidation of identifying super-spreader by k-shell decomposition , 2014, Scientific Reports.

[9]  Jian Xu,et al.  Social network user influence sense-making and dynamics prediction , 2014, Expert Syst. Appl..

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

[11]  Shaojie Tang,et al.  You Can Act Locally With Efficiency: Influential User Identification in Mobile Social Networks , 2017, IEEE Access.

[12]  Kasturi Dewi Varathan,et al.  Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method , 2017 .

[13]  Qiang Guo,et al.  Locating influential nodes via dynamics-sensitive centrality , 2015, Scientific Reports.

[14]  Gui Xiaolin,et al.  A New Method of Identifying Influential Users in the Micro-Blog Networks , 2017, IEEE Access.

[15]  Huan Liu,et al.  Community mining using three closely joint techniques based on community mutual membership and refinement strategy , 2017, Appl. Soft Comput..

[16]  Feng Jin,et al.  Identification of influential nodes in social networks with community structure based on label propagation , 2016, Neurocomputing.

[17]  Maxi San Miguel,et al.  A measure of individual role in collective dynamics , 2010, Scientific Reports.

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

[19]  Hassan Haghighi,et al.  INCIM: A community-based algorithm for influence maximization problem under the linear threshold model , 2016, Inf. Process. Manag..

[20]  Jorge E. Hirsch,et al.  An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship , 2009, Scientometrics.

[21]  Mohamed Bouguessa,et al.  Identifying Authorities in Online Communities , 2015, ACM Trans. Intell. Syst. Technol..

[22]  Marco Rosa,et al.  Four degrees of separation , 2011, WebSci '12.