Effective and efficient location influence mining in location-based social networks

Location-based social networks (LBSN) are social networks complemented with location data such as geo-tagged activity data of its users. In this paper, we study how users of an LBSN are navigating between locations and based on this information we select the most influential locations. In contrast to existing works on influence maximization, we are not per se interested in selecting the users with the largest set of friends or the set of locations visited by the most users; instead, we introduce a notion of location influence that captures the ability of a set of locations to reach out geographically by utilizing their visitors as message carriers. We further capture the influence of these visitors on their friends in LBSNs and utilize them to predict the potential future location influence more accurately. We provide exact online algorithms and more memory efficient but approximate variants based on the HyperLogLog and the modified HyperLogLog sketch to maintain a data structure called Influence Oracle that allows to efficiently find a top-k set of influential locations. Experiments show that our new location influence notion favors diverse sets of locations with a large geographical spread and that our algorithms are efficient, scalable and allow to capture future location influence.

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

[2]  P. Flajolet,et al.  HyperLogLog: the analysis of a near-optimal cardinality estimation algorithm , 2007 .

[3]  Francisco J. Mata,et al.  Web 2.0, Social Networks and E-commerce as Marketing Tools , 2014, J. Theor. Appl. Electron. Commer. Res..

[4]  Wen-Chih Peng,et al.  Exploring Social Influence on Location-Based Social Networks , 2014, 2014 IEEE International Conference on Data Mining.

[5]  Bernhard Schölkopf,et al.  Influence Maximization in Continuous Time Diffusion Networks , 2012, ICML.

[6]  Shanika Karunasekera,et al.  Location-based social networking for obtaining personalised driving advice , 2015, SIGSPATIAL/GIS.

[7]  S. Selva Kumar,et al.  Analysis of information propagation in academic social networks , 2016, 2016 International Conference on Recent Trends in Information Technology (ICRTIT).

[8]  Edith Cohen,et al.  Sketch-based Influence Maximization and Computation: Scaling up with Guarantees , 2014, CIKM.

[9]  László Lovász,et al.  Review of the book by Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency , 2005, Oper. Res. Lett..

[10]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[11]  Toon Calders,et al.  Location Influence in Location-based Social Networks , 2017, WSDM.

[12]  Toon Calders,et al.  Predicting Visitors Using Location-Based Social Networks , 2018, 2018 19th IEEE International Conference on Mobile Data Management (MDM).

[13]  Franco Zambonelli,et al.  Extracting urban patterns from location-based social networks , 2011, LBSN '11.

[14]  Laks V. S. Lakshmanan,et al.  A Data-Based Approach to Social Influence Maximization , 2011, Proc. VLDB Endow..

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

[16]  Toon Calders,et al.  Information Propagation in Interaction Networks , 2017, EDBT.

[17]  Laks V. S. Lakshmanan,et al.  Discovering leaders from community actions , 2008, CIKM '08.

[18]  Mi-Yen Yeh,et al.  Influential Nodes in a One-Wave Diffusion Model for Location-Based Social Networks , 2013, PAKDD.

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

[20]  Yan Shi,et al.  A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity , 2012, Comput. Geosci..

[21]  Yi Lu,et al.  Path Problems in Temporal Graphs , 2014, Proc. VLDB Endow..

[22]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[23]  Nguyen Thanh Hai.  A novel approach for location promotion on location-based social networks , 2015, The 2015 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF).

[24]  Gang Chen,et al.  Evaluating geo-social influence in location-based social networks , 2012, CIKM.

[25]  Huan Liu,et al.  Data Analysis on Location-Based Social Networks , 2014 .

[26]  Le Song,et al.  Scalable Influence Estimation in Continuous-Time Diffusion Networks , 2013, NIPS.

[27]  Dimitris Sacharidis,et al.  Regionally influential users in location-aware social networks , 2014, SIGSPATIAL/GIS.

[28]  Ee-Peng Lim,et al.  Mining Business Competitiveness from User Visitation Data , 2015, SBP.

[29]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[30]  Wen-Chih Peng,et al.  Modeling User Mobility for Location Promotion in Location-based Social Networks , 2015, KDD.

[31]  Huan Liu,et al.  Exploring Social-Historical Ties on Location-Based Social Networks , 2012, ICWSM.

[32]  Henk F. Moed,et al.  Mapping of Science : Critical elaboration and new approaches, a case study in agricultural biochemistry , 1988 .

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

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

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

[36]  Torben Bach Pedersen,et al.  Scalable Processing of Location-Based Social Networking Queries , 2016, 2016 17th IEEE International Conference on Mobile Data Management (MDM).