Location-Aware Targeted Influence Blocking Maximization in Social Networks

In this issue, we consider the location-aware targeted influence blocking maximization (LTIBM) problem, which plays a very important role in viral marketing and rumor control. LTIBM aims to find a set of positive seeds in a given social network to block the influence propagation of negative seeds over the targeted nodes located in a given region and having a preference on a given topic set as much as possible. We devise a simulation-based greedy algorithm based on monotone and submodular characteristics of influence function under the homogeneous independent cascade model. To improve the efficiency of the greedy algorithm, we propose LTIBM-H, a heuristic algorithm based on QT-tree and maximum influence arborescence (MIA). Experimental results show that the proposed LTIBM-H algorithm can achieve matching the blocking effect to the greedy algorithm and often performs better in terms of effectiveness than other baseline algorithms, while LTIBM-H is four orders of magnitude faster than the greedy algorithm.

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

[2]  Nicola Barbieri,et al.  Online Topic-aware Influence Maximization Queries , 2014, EDBT.

[3]  Li Pan,et al.  Scalable influence blocking maximization in social networks under competitive independent cascade models , 2017, Comput. Networks.

[4]  Kian-Lee Tan,et al.  Efficient location-aware influence maximization , 2014, SIGMOD Conference.

[5]  LiJin,et al.  Robust Influence Blocking Maximization in Social Networks , 2016 .

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

[7]  Yifei Yuan,et al.  Scalable Influence Maximization in Social Networks under the Linear Threshold Model , 2010, 2010 IEEE International Conference on Data Mining.

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

[9]  Christian Borgs,et al.  Maximizing Social Influence in Nearly Optimal Time , 2012, SODA.

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

[11]  Shuai Xu,et al.  Location-Based Influence Maximization in Social Networks , 2015, CIKM.

[12]  Jinhui Tang,et al.  Online Topic-Aware Influence Maximization , 2015, Proc. VLDB Endow..

[13]  Nicola Barbieri,et al.  Topic-Aware Social Influence Propagation Models , 2012, ICDM.

[14]  Wei Chen,et al.  Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model , 2011, SDM.

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

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

[17]  Xiaojiang Du,et al.  Location-Aware Influence Blocking Maximization in Social Networks , 2018, IEEE Access.

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

[19]  Wei Chen,et al.  Real-Time Topic-Aware Influence Maximization Using Preprocessing , 2015, CSoNet.

[20]  Laks V. S. Lakshmanan,et al.  Information and Influence Propagation in Social Networks , 2013, Synthesis Lectures on Data Management.

[21]  Xiang Cheng,et al.  Community-based seeds selection algorithm for location aware influence maximization , 2018, Neurocomputing.

[22]  Xuemin Lin,et al.  Distance-aware influence maximization in geo-social network , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[23]  Kyomin Jung,et al.  IRIE: Scalable and Robust Influence Maximization in Social Networks , 2011, 2012 IEEE 12th International Conference on Data Mining.

[24]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.