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Abstract Social influence is a fundamental issue in social network analysis and has attracted tremendous attention. However, existing research mainly focuses on studying peer influence. The method proposed is to… Click to show full abstract
Abstract Social influence is a fundamental issue in social network analysis and has attracted tremendous attention. However, existing research mainly focuses on studying peer influence. The method proposed is to analyze the degree of influence between nodes in a low-density network, and then mine structural influence and predict the degree of affect between the center node and others. We evaluate the proposed algorithm on both synthetic and real large networks. Experimental results show that our proposed algorithm has better performance than several alternative algorithms.
Journal Title: Physica A: Statistical Mechanics and its Applications
Year Published: 2019
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