A most influential node group discovery method for influence maximization in social networks: A trust-based perspective

摘要

Developing a computational method for discovering the most influential nodes in social networks is a significant challenge that reveals an approach for maximizing the influence diffusion. To improve the influence degree evaluation mechanism, we propose a trust-based most influential node discovery (TMID) method for discovering influential nodes in a social network. Four phases are performed to establish influence degrees for influential node discovery: (1) an influence propagation process, which reveals the influence diffusion records among nodes for obtaining the categories of nodes in the social network; (2) a trust evaluation method, which provides methods for calculating two types of trust relationships among users, namely, direct trust and indirect trust; (3) an influence evaluation phase, which calculates the explicit binary influence among users (named active influence), the potential binary influence among users (named inactive influence), and the unary influence of nodes (named node influence); and (4) a set of algorithms for discovering the most influential nodes, which comprise two phases: a heuristic phase and a greedy phase. We also list the results of a series of simulation tests for evaluating the performance of our mechanism.