TY - GEN
T1 - Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships
AU - Li, Yanhua
AU - Chen, Wei
AU - Wang, Yajun
AU - Zhang, Zhi Li
PY - 2013
Y1 - 2013
N2 - Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied because of their impacts on enabling effective online viral marketing. Existing studies focus on social networks with only friendship relations, whereas the foe or enemy relations that commonly exist in many OSNs, e.g., Epinions and Slashdot, are completely ignored. In this paper, we make the first attempt to investigate the influence diffusion and influence maximization in OSNs with both friend and foe relations, which are modeled using positive and negative edges on signed networks. In particular, we extend the classic voter model to signed networks and analyze the dynamics of influence diffusion of two opposite opinions. We first provide systematic characterization of both short-term and long-term dynamics of influence diffusion in this model, and illustrate that the steady state behaviors of the dynamics depend on three types of graph structures, which we refer to as balanced graphs, anti-balanced graphs, and strictly unbalanced graphs. We then apply our results to solve the influence maximization problem and develop efficient algorithms to select initial seeds of one opinion that maximize either its short-term influence coverage or long-term steady state influence coverage. Extensive simulation results on both synthetic and real-world networks, such as Epinions and Slashdot, confirm our theoretical analysis on influence diffusion dynamics, and demonstrate that our influence maximization algorithms perform consistently better than other heuristic algorithms.
AB - Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied because of their impacts on enabling effective online viral marketing. Existing studies focus on social networks with only friendship relations, whereas the foe or enemy relations that commonly exist in many OSNs, e.g., Epinions and Slashdot, are completely ignored. In this paper, we make the first attempt to investigate the influence diffusion and influence maximization in OSNs with both friend and foe relations, which are modeled using positive and negative edges on signed networks. In particular, we extend the classic voter model to signed networks and analyze the dynamics of influence diffusion of two opposite opinions. We first provide systematic characterization of both short-term and long-term dynamics of influence diffusion in this model, and illustrate that the steady state behaviors of the dynamics depend on three types of graph structures, which we refer to as balanced graphs, anti-balanced graphs, and strictly unbalanced graphs. We then apply our results to solve the influence maximization problem and develop efficient algorithms to select initial seeds of one opinion that maximize either its short-term influence coverage or long-term steady state influence coverage. Extensive simulation results on both synthetic and real-world networks, such as Epinions and Slashdot, confirm our theoretical analysis on influence diffusion dynamics, and demonstrate that our influence maximization algorithms perform consistently better than other heuristic algorithms.
KW - influence maximization
KW - signed social networks
KW - voter model
UR - http://www.scopus.com/inward/record.url?scp=84874263116&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874263116&partnerID=8YFLogxK
U2 - 10.1145/2433396.2433478
DO - 10.1145/2433396.2433478
M3 - Conference contribution
AN - SCOPUS:84874263116
SN - 9781450318693
T3 - WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining
SP - 657
EP - 666
BT - WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining
T2 - 6th ACM International Conference on Web Search and Data Mining, WSDM 2013
Y2 - 4 February 2013 through 8 February 2013
ER -