Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Hi-index | 0.00 |
Influence analysis and expert finding have received a great deal of attention in social networks. Most of existing works, however, aim to maximize influence based on communities structure in social networks. They ignored the location information, which often imply abundant information about individuals or communities. In this paper, we propose Info-Cluster, an innovative concept to describe how the information originated from a location cluster propagates in or between communities. According to this concept, we propose a framework for identifying the Info-Cluster in social networks, which uses both location information and communities structure. Taking the location information into consideration, we first adopt the K-Means algorithm to find location clusters. Next, we identify the communities for the whole network data set. Given the location clusters and communities, we present the information propagation based Info-Cluster detection algorithm. Experiments on Renren networks show that our method can reveal many meaningful results about regional influence analysis.