一种新的基于社区结构的影响最大化方法

基于传播网络的结构性,提出一种新的基于社区结构的影响最大化方法AMICS.该方法先利用已有社区挖掘算法识别出隐藏在网络中的社区结构,然后迭代选择跨越社区数最多的k个节点作为影响的初始传播者最大化影响的社区覆盖.在小型网络和中等规模网络数据集上的实验表明,该算法比传统的影响最大化方法更具优势.

A New Approach to Maximizing the Spread of Influence Based on Community Structure

Considering the structure of diffusion network, we proposed a new approach to maximizing the spread of influence based on community structure (AMICS). Our approach employs the community algorithm such as Radicchi’s algorithm/ICS algorithm to detect the community structure hidden in the network firstly, then iteratively chooses k important nodes which span the maximum communities to maximize the influence’s community coverage. The experiments on the small network and medium network show that AMICS is feasible andeffective.