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A dynamic algorithm based on cohesive entropy for influence maximization in social networks

机译：一种基于凝聚力熵的动态算法，用于社交网络中最大化的影响

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摘要

The problem of influence maximization in social networks has been widely investigated, but most previous studies have usually ignored the dynamic nature of propagation and the effects of local aggregation factors on diffusion. This paper presents a Dynamic algorithm based on cohesive Entropy for Influence Maximization (DEIM), the goal of which is to find the most influential nodes in social networks. Firstly, the Community Overlap Propagation Algorithm based on Cohesive Entropy (CECOPA) is put forward for the discovery of overlapping communities in networks, and potential nodes in the gathering area are selected to construct the candidate seed set. Then, the Optional Dynamic influence Propagation algorithm (ODP) is designed based on narrowing down the selection range of seeds. It utilizes a variety of entropy calculations to obtain the cohesive power between neighboring nodes and then determines whether the node has the ability to become a propagable pioneer of another node; thus, information continues to diffuse effectively. Finally, via many times experiments on several data sets, it is confirmed that the proposed DEIM algorithm in this paper can successfully affect the ideal number of users in different scenarios.
机译：社会网络中影响最大化的问题已被广泛调查，但最先前的研究通常忽视了传播的动态性质和局部聚集因子对扩散的影响。本文介绍了一种基于凝聚力熵的动态算法，用于影响最大化（DEIM），其目标是在社交网络中找到最有影响力的节点。首先，提出了基于凝聚熵（CECOPA）的社区重叠传播算法，用于发现网络中的重叠社区，并且选择收集区域中的潜在节点以构建候选种子集。然后，根据缩小种子的选择范围缩小，设计了可选的动态影响传播算法（ODP）。它利用各种熵计算来获得相邻节点之间的凝聚力，然后确定节点是否具有成为另一个节点的传播先驱的能力;因此，信息继续有效地扩散。最后，通过多次在几个数据集上实验，确认本文中所提出的DEIM算法可以成功地影响不同场景中的理想用户数。

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