Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
P-Rank: a comprehensive structural similarity measure over information networks
Proceedings of the 18th ACM conference on Information and knowledge management
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
| Hi-index | 0.00 |
In the social network research, the studies on social influence maximization and entity similarity are two important and orthogonal tasks. On homogeneous networks, social influence maximization research tries to identify an initial influential set that maximizes the spread of the information, while similarity studies focus on designing meaningful ways to quantify entities' similarities. When heterogeneous networks are becoming ubiquitous and entities of different types are related to each other, we observe the possibility of merging the two directions together to improve the performance for both of them. In fact, we found that influence values among one type of nodes and similarity scores among the other type of nodes reinforce each other towards better and more meaningful results. Therefore, we introduce a framework that computes social influence for one type of nodes and simultaneously measures similarity of the other type of nodes in a heterogeneous network. First, we decouple the target heterogeneous network (or we call it Influence Similarity (IS) network) into three different parts: Influence network, Similarity network and information tunnels (IT) between them. Through IT, we exchange the influence scores and the similarity scores to calculate more precise similarity and influence scores in order to improve both of their qualities. The experiment results on real world data shows that our framework enables influence maximization framework to identify more influential seeds in Influence network and similarity measures to produce more meaningful similarity scores in Similarity network simultaneously.