Finding Influential Nodes in Multiplex Networks Using a Memetic Algorithm

In order to find the nodes with better propagation ability, a large body of studies on the influence maximization problem has been conducted. Several influence spreading models and corresponding optimization algorithms have been proposed and successfully identified the infusive seeds in single isolated networks. However, as indicated by some recent studies and online materials, modern networked systems tend to have more complicated structures and multiple layers, which makes it difficult for existing seed determination techniques to deal with these networks. Thus, finding influential nodes in realistic multiplex networks remains open. Therefore, this paper aims to design an extended influence spreading model to simulate the influence diffusion process in multiplex networks, based on which a memetic algorithm is developed to find the seeds that are influential in all network layers. Experimental results on synthetic and real-world networks validate the effectiveness of the proposed algorithm. These results are helpful for identifying potential propagators in multiplex social networks, and provide solutions to analyze and gain deeper insights into networked systems.

[1]  Jim E. Smith,et al.  Coevolving Memetic Algorithms: A Review and Progress Report , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Yifeng Zeng,et al.  Structured Memetic Automation for Online Human-Like Social Behavior Learning , 2017, IEEE Transactions on Evolutionary Computation.

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

[4]  Erik Cambria,et al.  Sentic Computing for social media marketing , 2012, Multimedia Tools and Applications.

[5]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

[6]  Fred W. Glover,et al.  Memetic Search for Identifying Critical Nodes in Sparse Graphs , 2017, IEEE Transactions on Cybernetics.

[7]  Jong-Wook Kim,et al.  Intelligent memetic algorithm using GA and guided MADS for the optimal design of Interior PM Synchronous Machine , 2010, Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation.

[8]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[9]  Yew-Soon Ong,et al.  Memetic Computation , 2019, Adaptation, Learning, and Optimization.

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

[11]  Xin Wang,et al.  Friend recommendation with content spread enhancement in social networks , 2015, Inf. Sci..

[12]  Dong Xu,et al.  Influence Spreading Path and Its Application to the Time Constrained Social Influence Maximization Problem and Beyond , 2014, IEEE Transactions on Knowledge and Data Engineering.

[13]  Shuai Wang,et al.  Constructing Robust Cooperative Networks using a Multi-Objective Evolutionary Algorithm , 2017, Scientific Reports.

[14]  Cameron Marlow,et al.  A 61-million-person experiment in social influence and political mobilization , 2012, Nature.

[15]  Masahiro Kimura,et al.  Super mediator - A new centrality measure of node importance for information diffusion over social network , 2016, Inf. Sci..

[16]  Suh-Yin Lee,et al.  CIM: Community-Based Influence Maximization in Social Networks , 2014, TIST.

[17]  Jing Liu,et al.  A Multi-Objective Evolutionary Algorithm for Promoting the Emergence of Cooperation and Controllable Robustness on Directed Networks , 2018, IEEE Transactions on Network Science and Engineering.

[18]  Jong-Ryul Lee,et al.  A fast approximation for influence maximization in large social networks , 2014, WWW.

[19]  Jianshe Wu,et al.  Game-Based Memetic Algorithm to the Vertex Cover of Networks , 2019, IEEE Transactions on Cybernetics.

[20]  Susan K. Walker.  Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives , 2011 .

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

[22]  Kay Chen Tan,et al.  A Multi-Facet Survey on Memetic Computation , 2011, IEEE Transactions on Evolutionary Computation.

[23]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

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

[25]  Jianhua Ma,et al.  PPRank: Economically Selecting Initial Users for Influence Maximization in Social Networks , 2017, IEEE Systems Journal.

[26]  Laks V. S. Lakshmanan,et al.  CELF++: optimizing the greedy algorithm for influence maximization in social networks , 2011, WWW.

[27]  Guanrong Chen,et al.  Compressive-Sensing-Based Structure Identification for Multilayer Networks , 2018, IEEE Transactions on Cybernetics.

[28]  John F. Padgett,et al.  Robust Action and the Rise of the Medici, 1400-1434 , 1993, American Journal of Sociology.

[29]  Sang-Wook Kim,et al.  Efficient and effective influence maximization in social networks: A hybrid-approach , 2018, Inf. Sci..

[30]  Jinghua Zhu,et al.  A New Structure-Hole-Based Algorithm For Influence Maximization in Large Online Social Networks , 2017, IEEE Access.

[31]  Conrado J. Pérez Vicente,et al.  Diffusion dynamics on multiplex networks , 2012, Physical review letters.

[32]  Maoguo Gong,et al.  Memetic algorithm for community detection in networks. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  Johan Kwisthout,et al.  Community-based influence maximization in social networks under a competitive linear threshold model , 2017, Knowl. Based Syst..

[34]  Jing Liu,et al.  A Two-Phase Multiobjective Evolutionary Algorithm for Enhancing the Robustness of Scale-Free Networks Against Multiple Malicious Attacks , 2017, IEEE Transactions on Cybernetics.

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

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

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

[38]  Jing Liu,et al.  A memetic algorithm for enhancing the robustness of scale-free networks against malicious attacks , 2014 .

[39]  Xianglong Tang,et al.  Mitigate Cascading Failures on Networks using a Memetic Algorithm , 2016, Scientific Reports.

[40]  Hernán A. Makse,et al.  Influence maximization in complex networks through optimal percolation , 2015, Nature.

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

[42]  Maoguo Gong,et al.  Influence maximization in social networks based on discrete particle swarm optimization , 2016, Inf. Sci..

[43]  Huiling Zhang,et al.  Least Cost Influence Maximization Across Multiple Social Networks , 2016, IEEE/ACM Transactions on Networking.

[44]  Ali Moeini,et al.  A fast algorithm for finding most influential people based on the linear threshold model , 2015, Expert Syst. Appl..

[45]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[46]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[47]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.