A New Node Centrality Evaluation Model for Multi-Community Weighted Social Networks

摘要

Identifying key nodes is an important research issue in social networks. Most of current social networks are weighted networks as well as consist of multiple communities. A suitable centrality measure for weighted social networks should be capable of finding most important nodes in each community. However, based on the existing centrality measure for weighted social networks, the most influential nodes are closely gathered in one community or distribute in a portion of all communities. In this paper, we propose a Tie Strength Matrix based Principal Component Centrality (TSM-based PCC), which extends PCC, a centrality measure for unweighted networks, to weighted social networks. Experiment results show that, based on TSM-based PCC influential nodes can be picked out accurately in real social network datasets. Furthermore, TSM-based PCC outperforms other centrality measures in identifying important nodes in each community. Hence the proposed TSM- based PCC is feasible and effective in weighted social networks.