Abstract:

Personalized influence maximization in social network has become a new branch of influence maximization study in recent years. Different from existing research that assumes equal propagating strengths of social network edges, our work aims to find out the topk most influential nodes for the target user without inappropriate assumption. We propose a maximizedinfluencepath algorithm (MIPA) based on the independent cascade model. It solves the problem through three stages. Firstly, to compute the propagating strengths from the nodes of social network to the neighbors of the target node, the strengths of edges are transformed into its logarithmic form for getting the maximized influence paths. Secondly, the strength of maximized influence paths which pass through different neighbors with the same source nodes are consolidated to calculate the node’s propagating strength on the target node. Finally, the seed set with high propagating strength is found out by selecting the topk nodes. We testify the algorithm on several realworld social networks. Experimental results validate the proposed algorithm.

Key words: social network;personalization;influence maximization;target user