一种改进的基于群体的社交网络影响力最大化方法

Danhua Huang, Li Pan
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引用次数: 4

摘要

影响最大化是社会网络分析中的一个经典问题。这个问题是np困难的,可以用贪心算法来解决。然而,该方法需要成千上万的蒙特卡罗模拟,非常耗时且不可扩展。为了改进该方法,研究人员提出了基于社区的影响力最大化方法。然而,该方法基于节点连接检测社区,而通常忽略了节点的影响属性。此外,在计算基于社区结构的影响传播时,忽略了社区规模和边界节点。为了改进基于社区的影响力最大化方法,本文首先根据节点的影响力属性寻找具有相似影响力特征的群体。然后在考虑群体规模和边界节点的基础上,根据群体结构近似计算影响范围。实验表明,在匹配运行时间的情况下,本文基于群体的影响力最大化方法比相应的基于社区的影响力最大化方法具有更好的影响力传播。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Improved Group-based Influence Maximization Method in Social Networks
Influence maximization is a classic problem studied in social network analysis. This problem is NP-hard and can be solved with a greedy algorithm. However, the method requires tens of thousands of Monte-Carlo simulations, which are very time consuming and not scalable. To improve this method, researchers presented the community-based influence maximization method. However, this method detects communities based on node connections while generally ignores the influence property of nodes. In addition, when computing the influence spread based on community structure, it loses sight of the community size and border nodes. To improve the community-based influence maximization method, this paper first finds groups with similar influence characteristics based on the influence property of nodes. Then influence spread is approximately calculated based on the group structure in which the group size and border nodes are considered. Experiments demonstrate that the group-based influence maximization method in this paper achieves better influence spread than corresponding community-based influence maximization methods with matching running time.
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