An Approximation Algorithm for Active Friending in Online Social Networks

Guiding users to actively expanding their online social circles is one of the primary strategies for enhancing user participation and growing online social networks. In this paper, we study the active friending problem which aims at providing users with the strategy for methodically sending invitations to successfully build a friendship with target users. We consider the prominent linear threshold model for the friending process and formulate the active friending problem as an optimization problem. The key observation is the relationship between the active friending problem and the minimum subset cover problem, based on which we present the first randomized algorithm with a data-independent approximation ratio and a controllable success probability for general graphs. The performance of the proposed algorithm is theoretically analyzed and supported by encouraging simulation results done on extensive datasets.

[1]  Jure Leskovec,et al.  {SNAP Datasets}: {Stanford} Large Network Dataset Collection , 2014 .

[2]  Xiang Li,et al.  Privacy Issues in Light of Reconnaissance Attacks with Incomplete Information , 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[3]  G. A. Tijssen,et al.  The Data-Correcting Algorithm for the Minimization of Supermodular Functions , 1999 .

[4]  Ding-Zhu Du,et al.  How Could a Boy Influence a Girl? , 2014, 2014 10th International Conference on Mobile Ad-hoc and Sensor Networks.

[5]  Kamal Kant Bharadwaj,et al.  A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity , 2012, Social Network Analysis and Mining.

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

[7]  Xing Xie,et al.  Potential Friend Recommendation in Online Social Network , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[8]  Cheng-Chi Wang,et al.  Friend Recommendation for Location-Based Mobile Social Networks , 2013, 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[9]  Jan Vondrák,et al.  Optimal approximation for submodular and supermodular optimization with bounded curvature , 2013, SODA.

[10]  Michael J. Muller,et al.  Make new friends, but keep the old: recommending people on social networking sites , 2009, CHI.

[11]  Weili Wu,et al.  The complexity of influence maximization problem in the deterministic linear threshold model , 2012, J. Comb. Optim..

[12]  John Hannon,et al.  Recommending twitter users to follow using content and collaborative filtering approaches , 2010, RecSys '10.

[13]  Richard M. Karp,et al.  An optimal algorithm for Monte Carlo estimation , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.

[14]  Laks V. S. Lakshmanan,et al.  SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model , 2011, 2011 IEEE 11th International Conference on Data Mining.

[15]  Yi Li,et al.  Active Friending in Online Social Networks , 2017, BDCAT.

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

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

[18]  Ding-Zhu Du,et al.  Beyond Uniform Reverse Sampling: A Hybrid Sampling Technique for Misinformation Prevention , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[19]  Wei Chen,et al.  Maximizing acceptance probability for active friending in online social networks , 2013, KDD.

[20]  Moran Feldman,et al.  Constrained Monotone Function Maximization and the Supermodular Degree , 2014, APPROX-RANDOM.

[21]  Sandra L. Calvert,et al.  College students' social networking experiences on Facebook , 2009 .

[22]  Bin Liu,et al.  An Efficient Randomized Algorithm for Rumor Blocking in Online Social Networks , 2020, IEEE Transactions on Network Science and Engineering.

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

[24]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[25]  Rajeev Motwani,et al.  Randomized Algorithms , 1995, SIGA.

[26]  Jaideep Srivastava,et al.  A Generalized Linear Threshold Model for Multiple Cascades , 2010, 2010 IEEE International Conference on Data Mining.

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

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

[29]  Xiang Li,et al.  Adaptive Crawling with Multiple Bots: A Matroid Intersection Approach , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[30]  Xiang Li,et al.  Adaptive Reconnaissance Attacks with Near-Optimal Parallel Batching , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

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

[32]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[33]  Michael Dinitz,et al.  The Densest k-Subhypergraph Problem , 2016, APPROX-RANDOM.