一种面向团体的影响最大化方法

影响最大化旨在从给定社会网络中寻找出一组影响力最大的子集.现有工作大都在假设实体点（个人或博客等）影响关系已知的情况下，关注于分析单个实体点的影响力.然而在一些实际场景中，人们往往更关注区域或人群等这类团体的组合影响力，如：户外广告，电视营销，疫情防控等.本文研究了影响力团体的选择问题：（1）基于团体的关联发现，我们建立了团体传播模型GIC（Group Independent Cascade）；（2）根据GIC模型，我们给出了贪心算法CGIM（Cascade Group influence maximization）搜索最具影响力的top-k团组合.在人工数据和真实数据上，实验验证了我们方法的效果和效率.

Group-Based Method for Influence Maximization

Influence maximization aims at finding a set of influential individuals (i.e. users, blog etc.) in a social network. Most of the existing work focused on the influence of individuals under the hypothesis that the influence relationship between the individuals is known in advance. Nonetheless, it is often the case that groups (i.e. area, crowd etc.) are only natural targets of initial convincing attempts in many real-world scenarios, e.g., billboards, television marketing and plague prevention, etc. In this paper, we address the problem of locating the most influential groups in a network. (1) Based on the discovery of the group associations, we propose GIC(Group Independent Cascade) model to simulate the influence propagation process at the group granularity. (2) We also introduce a greedy algorithm called CGIM(Cascade Group influence maximization) to determine the top-k influential groups under GIC model. Experimental results on both synthetic and real datasets verify the effectiveness and efficiency of our method.