社交网络中基于贝叶斯和半环代数模型的节点影响力计算机理

本文综合考虑网络结构及节点间的互动等关键因素, 提出了一种节点影响力分布式计算机理. 首先根据节点交互行为在时域上的自相似特性, 运用带折扣因子的贝叶斯模型计算节点间的直接影响力; 然后运用半环模型来分析节点间接影响力的聚合; 最后根据社交网络的小世界性质及传播门限, 综上计算出节点的综合影响力. 仿真结果表明, 本文给出的模型能有效抑制虚假粉丝导致的节点影响力波动, 消除了虚假粉丝的出现对节点影响力计算带来的干扰, 从中选择影响力高的若干节点作为传播源节点, 可以将信息传播到更多数目的节点, 促进了信息在社交网络中的传播. 关键词： 社交网络 影响力 贝叶斯 半环代数

Node influence calculation mechanism based on Bayesian and semiring algebraic model in social networks

In social networks, many applications and spreading depend on the nodes with high influence to do viral marketing, which indicates that nodes' influence should be measured in a comprehensive and reasonable way. The appearance of fake fans results in change of network topology and brings new challenge to topology-based traditional methods. This paper incorporates both the network topology and interactions among nodes into our new distribution mechanism of node influence calculation in social networks. Considering the similarity of node behaviors in time domain and several key factors, this paper presents by a discounted Bayesian model for direct influence between nodes at first. Then a semi-ring-based aggregation implements for indirect influence and the composite influence are obtained by the combination of both direct and indirect influences. Simulation shows that this mechanism not only performs well against fake fans attack and restrains the fluctuation of nodes' influence, but also spreads to more nodes when we choose several nodes with high influence under our method to be source nodes.
Keywords： social networks influence Bayesian semiring algebraic