The Influence Maximization Problem in the Network Under Node Personalized Characteristics

Considering the personalization of nodes in complex networks, we propose an algorithm that maximizes the influence of similarity based on overlapping nodes and nodes. In the proposed algorithm, we investigate the overlapping of communities in the network and use overlapped nodes as the initial propagation seed set. Then, we discuss the personalized characteristics of nodes and introduce the concept of sparse attributes. An improved independent cascaded model is built to integrate the similarity of node attributes. Finally, experiments are performed on real data sets. The results show that the proposed algorithm is better than others.

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