集体影响力最大化

Xudong Wu, Luoyi Fu, Keyi Wu, Bo Jiang, Xinbing Wang, Guihai Chen
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引用次数: 3

摘要

复杂现象中普遍存在的级联过程,使得识别被广泛认为是引发疫情的一小部分有影响的单位,一直是网络科学中的一个关键问题。这个np困难问题在2003年被表述为影响最大化(IM),已经收到了许多不同角度的启发式解决方案。然而,这些方法往往不能提供可靠的解决方案,因为缺乏一个精确的度量来评估单元对级联的贡献。在本文中,我们从最优渗透的角度来解决IM问题,并基于集体影响(CI)来评估单元,CI是一种反映结构凝聚力的新度量,反映了每个单元的邻居对整个网络中单元的集体动态的贡献。我们发现,在概率扩散模型下,每个节点的结构影响力(CI值)是某一跳内邻居扩散概率的加权累积。利用新定义的度量CI,我们提出了一种选择CI值最大的种子的IM算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collective Influence Maximization
The omnipresence of cascading process in complex phenomena makes the identification of a small set of influential units, which is widely believed to trigger the outbreak, always an crucial issue in network science. Formulated as Influence maximization (IM) in 2003, this NP-hard problem has received a multitude of heuristic solutions with diverse angles. However, these methods are often unable to provide reliable solutions, due to the lack of an exact metric for evaluating units' contributions on cascading. In this paper, we address IM from optimal percolation and evaluate units based on the collective influence (CI), a novel metric on structural cohesive power that reflects the contributions of each unit's neighborhood on shaping collective dynamics of units over whole network. We reveal that, under probabilistic diffusion model, the structural influence power (CI value) of each node is a weighted cumulation of the diffusion probabilities from neighbors within certain hop. With the newly formulated metric CI, we propose a novel IM algorithm which chooses seeds with the largest CI values.
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