Efficient algorithms based on centrality measures for identification of top-K influential users in social networks

Abstract The problem of influence maximization has grown in popularity and interest in recent decades due to its valuable application in various fields. This problem mainly focuses on identifying Top-K influential users that, when selected, the influence spread will be maximized. Thus, the identification of such nodes is pivotal in increasing the adoption of promoted information and behavior within the network. In this paper, we propose two new efficient algorithms, namely, “MinCDegKatz d-hops (MaxCDegKatz d-hops)” that relies on a combination of centralization measures due to their known efficiency and performance in terms of influence spread and their low runtime complexity. The proposed algorithms combine between the degree centrality as local measure and the Katz centrality as global centrality metric on a graph with preselected weight edges that should exceed a predefined threshold value. Thus, each selected seed set is separated by a number of hops that differ depending on the graph radius. Then, the influence spread is measured for the two proposed algorithms under the Independent Cascade (IC) and Linear Threshold (LT) models. We conducted extensive experiments on a large-scale graph that demonstrated the performance of our proposed algorithms against existing approaches in term of spreading ability and time complexity.

[1]  Wei Chen,et al.  Robust Influence Maximization , 2016, KDD.

[2]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[3]  David Kempe,et al.  Stability of influence maximization , 2014, KDD.

[4]  Xiaokui Xiao,et al.  Influence Maximization in Near-Linear Time: A Martingale Approach , 2015, SIGMOD Conference.

[5]  Daniel J. Brass,et al.  Network Analysis in the Social Sciences , 2009, Science.

[6]  John Scott.  What is social network analysis , 2010 .

[7]  Tiejun Lv,et al.  A Novel Centrality Cascading Based Edge Parameter Evaluation Method for Robust Influence Maximization , 2017, IEEE Access.

[8]  Sheng Huang,et al.  Efficient Methods to Select Top-K propagators based on Distance and Radius Neighbor , 2018 .

[9]  Shishir Bharathi,et al.  Competitive Influence Maximization in Social Networks , 2007, WINE.

[10]  Gypsy Nandi,et al.  A Novel Hybrid Approach for Influence Maximization in Online Social Networks Based on Node Neighborhoods , 2018 .

[11]  Luciano da Fontoura Costa,et al.  The role of centrality for the identification of influential spreaders in complex networks , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  C. Piccardi,et al.  Complexity, centralization, and fragility in economic networks , 2018, PloS one.

[13]  My T. Thai,et al.  Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks , 2016, SIGMOD Conference.

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

[15]  Xiaokui Xiao,et al.  Influence maximization: near-optimal time complexity meets practical efficiency , 2014, SIGMOD Conference.

[16]  Michele Benzi,et al.  Total communicability as a centrality measure , 2013, J. Complex Networks.

[17]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[18]  Radu Grosu,et al.  HellRank: a Hellinger-based centrality measure for bipartite social networks , 2016, Social Network Analysis and Mining.

[19]  Laizhong Cui,et al.  DDSE: A novel evolutionary algorithm based on degree-descending search strategy for influence maximization in social networks , 2018, J. Netw. Comput. Appl..

[20]  Nicole Immorlica,et al.  Influence Maximization with an Unknown Network by Exploiting Community Structure , 2017, SocInf@IJCAI.

[21]  Jie Tang,et al.  Influential Node Tracking on Dynamic Social Network: An Interchange Greedy Approach , 2017, IEEE Transactions on Knowledge and Data Engineering.

[22]  Wei Chen,et al.  Real-time topic-aware influence maximization using preprocessing , 2014, Computational Social Networks.

[23]  Michalis Vazirgiannis,et al.  MATI: An efficient algorithm for influence maximization in social networks , 2017, PloS one.

[24]  Zhen Lin,et al.  Influence maximization in social networks under an independent cascade-based model , 2016 .

[25]  Wei Chen,et al.  Interplay between Social Influence and Network Centrality: A Comparative Study on Shapley Centrality and Single-Node-Influence Centrality , 2016, WWW.

[26]  Ahmed Sameh,et al.  Top-K influential users selection based on combined Katz centrality and propagation probability , 2018, 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA).

[27]  Harry Eugene Stanley,et al.  Correlation between centrality metrics and their application to the opinion model , 2014, The European Physical Journal B.

[28]  Ruocheng Guo,et al.  The independent cascade and linear threshold models , 2015 .

[29]  Maoguo Gong,et al.  An Efficient Memetic Algorithm for Influence Maximization in Social Networks , 2016, IEEE Computational Intelligence Magazine.

[30]  Li Guo,et al.  An upper bound based greedy algorithm for mining top-k influential nodes in social networks , 2014, WWW '14 Companion.

[31]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[32]  Xiaohui Zhao,et al.  Evaluating Influential Nodes in Social Networks by Local Centrality with a Coefficient , 2017, ISPRS Int. J. Geo Inf..

[33]  Silvio Lattanzi,et al.  The Power of Random Neighbors in Social Networks , 2015, WSDM.

[34]  Jacob Goldenberg,et al.  Using Complex Systems Analysis to Advance Marketing Theory Development , 2001 .

[35]  Thang N. Dinh,et al.  Cost-aware Targeted Viral Marketing in billion-scale networks , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.