关键词: 社交网络, 影响力最大化, 边缘贡献, 启发式算法

Abstract: Influence maximization is a problem of obtaining a subset of nodes in social network to maximize the influence spread. Aiming at the problem of the poor accuracy of heuristic algorithm, existing works consider the overlapped range, and ignore the problem of edge contributions. This paper focuses on how to select a seed set that has the maximum influence based on edge contributions. The algorithm evaluates the influence of information spread by calculating the global influence and adjacent influence. Then it removes the selected node influence range and updates the network to eliminate the interference of edge contributions to node influence evaluation. Finally, this paper proposes an edge-cover algorithm for influence maximization based on independent cascade model. The experimental results show that the proposed algorithm has a greater impact on the spread of range.

Key words:  social network, influence maximization, edge contributions, heuristic algorithm