Information spread link prediction through multi-layer of social network based on trusted central nodes

In this study, a path prediction method based on trusted central nodes is proposed for information flow transmission among multi-layer of social network. With the complex, sensitive and the burn-in of information protection strategies, the regulation and control of information flow transmission is becoming difficult in social network. By exacting the trusted central nodes from the community in social network, the feedback mechanism is used to realize the time-varying selecting of trusted central nodes. Then, an information spread link model for multi-layer of social network is obtained through the trusted central nodes. Finally, the shortest transmission path among layers of social network is calculated. The experimental results show that time-varying selection strategy of trusted central nodes restrains the rumor transmission which increases the reliability of information in social network. The information spread link algorithm for multi-layer of social network can reduce the path length and transmission time and improve the transmission efficiency.

[1]  P. Uma Maheswari,et al.  An Energy Efficient Cluster Head Selection Technique Using Network Trust and Swarm Intelligence , 2016, Wireless Personal Communications.

[2]  Shi-Hua Zhang,et al.  Quantitative function and algorithm for community detection in bipartite networks , 2015, Inf. Sci..

[3]  S. Thurner,et al.  The multi-layer network nature of systemic risk and its implications for the costs of financial crises , 2015, 1505.04276.

[4]  Nathan Albin,et al.  Network clustering and community detection using modulus of families of loops. , 2016, Physical review. E.

[5]  Jiguo Yu,et al.  Cost-Efficient Strategies for Restraining Rumor Spreading in Mobile Social Networks , 2017, IEEE Transactions on Vehicular Technology.

[6]  Kiseon Kim,et al.  Mean-Field Dynamics of Inter-Switching Memes Competing Over Multiplex Social Networks , 2017, IEEE Communications Letters.

[7]  David F. Nettleton,et al.  Data mining of social networks represented as graphs , 2013, Comput. Sci. Rev..

[8]  Cristopher Moore,et al.  Community detection, link prediction, and layer interdependence in multilayer networks , 2017, Physical review. E.

[9]  Yong Zhang,et al.  Research on cascading failure in multilayer network with different coupling preference , 2017 .

[10]  Shreyas Sundaram,et al.  The Strategic Formation of Multi-Layer Networks , 2015, IEEE Transactions on Network Science and Engineering.

[11]  Xiufen Zou,et al.  Identifying key nodes in multilayer networks based on tensor decomposition. , 2017, Chaos.

[12]  D. Quail,et al.  Obesity and the tumor microenvironment , 2017, Science.

[13]  James Cheng,et al.  Fast PageRank approximation by adaptive sampling , 2013, Knowledge and Information Systems.

[14]  Dong Liu,et al.  Optimal multi-community network modularity for information diffusion , 2016 .

[15]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[16]  Hwan-Seok Yang,et al.  A study on hybrid trust evaluation model for identifying malicious behavior in mobile P2P , 2016, Peer Peer Netw. Appl..

[17]  Kristina Lerman,et al.  Rethinking Centrality: The Role of Dynamical Processes in Social Network Analysis , 2012, ArXiv.

[18]  Jinho Lee,et al.  Optimization of stochastic virus detection in contact networks , 2015, Oper. Res. Lett..

[19]  Robert H. Anderson,et al.  Identification of a hybrid myocardial zone in the mammalian heart after birth , 2017, Nature Communications.

[20]  Soodeh Hosseini,et al.  A model for malware propagation in scale-free networks based on rumor spreading process , 2016, Comput. Networks.

[21]  A. Arenas,et al.  Mathematical Formulation of Multilayer Networks , 2013, 1307.4977.

[22]  Alexandros Nanopoulos,et al.  Link injection for boosting information spread in social networks , 2014, Social Network Analysis and Mining.

[23]  Sergio Gómez,et al.  Random walk centrality in interconnected multilayer networks , 2015, ArXiv.

[24]  Sinan Aral,et al.  Exercise contagion in a global social network , 2017, Nature Communications.

[25]  Mason A. Porter,et al.  Lost in transportation: Information measures and cognitive limits in multilayer navigation , 2016, Science Advances.

[26]  Dashun Wang,et al.  Impact of human mobility on social networks , 2015, Journal of Communications and Networks.

[27]  Maxi San Miguel,et al.  Dynamical origins of the community structure of multi-layer societies , 2016, ArXiv.

[28]  Lada A. Adamic,et al.  Exposure to ideologically diverse news and opinion on Facebook , 2015, Science.

[29]  Francesco Folino,et al.  An Evolutionary Multiobjective Approach for Community Discovery in Dynamic Networks , 2014, IEEE Transactions on Knowledge and Data Engineering.

[30]  Wenjun Xu,et al.  Multi-layer based multi-path routing algorithm for maximizing spectrum availability , 2018, Wirel. Networks.

[31]  Kash Barker,et al.  Effects of multi-state links in network community detection , 2017, Reliab. Eng. Syst. Saf..

[32]  Enrique Herrera-Viedma,et al.  A visual interaction consensus model for social network group decision making with trust propagation , 2017, Knowl. Based Syst..

[33]  Yichuan Jiang,et al.  Cross-layers cascade in multiplex networks , 2014, Auton. Agents Multi Agent Syst..

[34]  Meng Wang,et al.  Trust Agent-Based Behavior Induction in Social Networks , 2016, IEEE Intelligent Systems.

[35]  Yu Yao,et al.  An immunization strategy for social network worms based on network vertex influence , 2015 .

[36]  Ji-Woong Choi,et al.  Pruning-Based Sparse Recovery for Electrocardiogram Reconstruction from Compressed Measurements , 2017, Sensors.

[37]  Zhenguo Chen,et al.  Trust Model of Wireless Sensor Networks and Its Application in Data Fusion , 2017, Sensors.

[38]  Manoj Kumar,et al.  The fuzzy based QMPR selection for OLSR routing protocol , 2014, Wirel. Networks.