Negative Influence Minimization Algorithm for Social Networks

doi:10.16182/j.issn1004731x.joss.19-0377

Abstract

Abstract: While positive information is spreading in social networks, there is still a large amount of negative information spreading in the network. Aiming at the fact that there is few researches on suppressing the spread of negative information, a negative influence minimization algorithm for social networks is proposed. When negative information appears in social networks and some initial nodes are infected, the behavior of nodes propagating information depends on its coordination game with neighbor nodes. The objective function with minimal influence is used to find the K optimal blocking nodes, and finally the size of the final infected node is minimized by blocking K uninfected nodes. The experimental results show that the proposed algorithm can better suppress the negative influence diffusion than the three benchmark algorithms.

Key words: social networks, influence minimization, blocking nodes, negative influence

CLC Number:

TP391.9

Yang Yi, Wu Chunxiao, He Ming, Zhou Bo. Negative Influence Minimization Algorithm for Social Networks[J]. Journal of System Simulation, 2021, 33(2): 501-508.

References

[1] Peng S, Zhou Y, Cao L, et al.Influence Analysis in Social Networks: A Survey[J]. Journal of Network and Computer Applications (S1084-8045), 2018, 106(3): 17-32.
[2] Wang Y, Fan Q, Li Y, et al.Real-time Influence Maximization on Dynamic Social Streams[J]. Proceedings of the VLDB Endowment(S2150-8097), 2017, 10(7): 805-816.
[3] Li Y, Zhang D, Tan K L.Real-time Targeted Influence Maximization for Online Advertisements[J]. Proceedings of the VLDB Endowment (S2150-8097), 2015, 8(10): 1070-1081.
[4] Nguyen H T, Dinh T N, Thai M T.Cost-aware Targeted Viral Marketing in Billion-scale Networks[C]// IEEE INFOCOM 2016-The 35^th Annual IEEE International Conference on Computer Communications. San Francisco, CA, USA: IEEE, 2016: 1-9.
[5] Li Y, Fan J, Zhang D, et al.Discovering Your Selling Points: Personalized Social Influential Tags Exploration[C]// 2017 ACM International Conference on Management of Data. Chicago, IL, USA: ACM, 2017: 619-634.
[6] Chen S, Fan J, Li G, et al.Online Topic-aware Influence Maximization[J]. Proceedings of the VLDB Endowment (S2150-8097), 2015, 8(6): 666-677.
[7] Chen X, Song G, He X, et al.On Influential Nodes Tracking in Dynamic Social Networks[C]// 2015 SIAM International Conference on Data Mining. Vancouver, BC, Canada: SIAM, 2015: 613-621.
[8] Ohsaka N, Akiba T, Yoshida Y, et al.Dynamic Influence Analysis in Evolving Networks[J]. Proceedings of the VLDB Endowment (S2150-8097), 2016, 9(12): 1077-1088.
[9] Tang Y, Shi Y, Xiao X.Influence Maximization in Near-linear Time: A martingale Approach[C]// 2015 ACM SIGMOD International Conference on Management of Data. New York, NY, USA: ACM, 2015: 1539-1554.
[10] Ohsaka N, Yamaguchi Y, Kakimura N, et al.Maximizing Time-decaying Influence in Social Networks[C]// Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2016. Riva del Garda, Italy,: Springer, Cham, 2016: 132-147.
[11] Gomez-Rodriguez M, Song L, Du N, et al.Influence Estimation and Maximization in Continuous-time Diffusion Networks[J]. ACM Transactions on Information Systems (TOIS)(S1046-8188), 2016, 34(2): 9.
[12] Xie M, Yang Q, Wang Q, et al.Dynadiffuse: A Dynamic Diffusion Model for Continuous Time Constrained Influence Maximization[C]// Twenty- Ninth AAAI Conference on Artificial Intelligence. Austin, Texas USA: AAAI Press, 2015.
[13] Wang B, Chen G, Fu L, et al.Drimux: Dynamic Rumor Influence Minimization with User Experience in Social Networks[J]. IEEE Transactions on Knowledge and Data Engineering (S1041-4347), 2017, 29(10): 2168-2181.
[14] Yao Q, Shi R, Zhou C, et al.Topic-aware Social Influence Minimization[C]// 24th International Conference on World Wide Web. Florence, Italy: ACM, 2015: 139-140.
[15] Liu Y, Han Z, Shi S, et al.SA-Min: An Efficient Algorithm for Minimizing the Spread of Influence in a Social Network[C]// Wireless Sensor Networks. CWSN 2017. Tianjin, China: Springer, Singapore, 2017: 333-343.
[16] Yao Q, Zhou C, Xiang L, et al.Minimizing the Negative Influence by Blocking Links in Social Networks[C]// International Conference on Trustworthy Computing and Services. Berlin, Heidelberg: Springer, 2014: 65-73.
[17] Zhang Y, Zhang Y.Top-K Influential Nodes in Social Networks: A Game Perspective[C]// 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2017: 1029-1032.
[18] Irfan M T, Ortiz L E.A Game-Theoretic Approach to Influence in Networks[C]// Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, California, USA, August 7-11, 2011. AAAI Press, 2011.
[19] Rossi R, Ahmed N.The Network Data Repository with Interactive Graph Analytics and Visualization[C]// Twenty-Ninth AAAI Conference on Artificial Intelligence. Austin, Texas USA: AAAI Press, 2015.