An Influence Measure of Nodes Based on Structures of Social Networks

LI Ze-peng; ZUO Yang; WANG Hong-yu

doi:10.3969/j.issn.0372-2112.2016.12.022

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An Influence Measure of Nodes Based on Structures of Social Networks

更新时间：2025-07-16

- An Influence Measure of Nodes Based on Structures of Social Networks
- Acta Electronica Sinica Vol. 44, Issue 12, Pages: 2967-2974(2016)
- 作者机构：
  
  1. 北京大学高可信软件技术教育部重点实验室,北京,100871
  2. 北京大学信息科学技术学院,北京,100871
  3. 北京大学高可信软件技术教育部重点实验室,北京,100871
  4. 北京大学信息科学技术学院,北京,100871
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.12.022
  CLC： TP399
- Published：2016
- 稿件说明：
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LI Ze-peng, ZUO Yang, WANG Hong-yu. An Influence Measure of Nodes Based on Structures of Social Networks[J]. Acta Electronica Sinica, 2016, 44(12): 2967-2974.
DOI：

LI Ze-peng, ZUO Yang, WANG Hong-yu. An Influence Measure of Nodes Based on Structures of Social Networks[J]. Acta Electronica Sinica, 2016, 44(12): 2967-2974. DOI： 10.3969/j.issn.0372-2112.2016.12.022.

摘要

度量社交网络节点影响力是社交网络结构分析的关键问题之一.目前研究社交网络节点影响力的方法主要有两大类：中心度方法和节点删除方法.前者主要通过度或最短路径等因素来判断节点的影响力，不考虑网络的连通性；后者通过节点删除后对网络结构的破坏程度来判断，计算复杂性很高，不适用于较大规模的社交网络.通过结合社交网络的局部连通度及节点间的最短路径，提出了连通中心度来度量社交网络中节点的影响力，并给出了连通中心度的计算方法和一些特殊网络中节点的连通中心度的值.最后，通过实验说明该指标能很好地度量社交网络中节点的影响力.

Abstract

Identifying the influence of nodes is one of the major research topics in the structural analysis of social networks.The current measures of researching the node influence can be divided into two categories:centrality measure and node removal measure.The former mainly identifies the influence of the node by degree or shortest path

without considering the connectivity of social networks; while the latter by the damage of the structure of a social network when some nodes are removed.The node removal measure is incapable to be applied in large-scale since the computational complexity.We propose a new parameter

connectedness centrality

to identify the influence of nodes in networks by combining the local connectivity and shortest paths.We give a method to compute the connectedness centrality of the node and obtain the precise values in some specific networks.Finally

an experiment using a real-word network shows that our method can well identify the influence of the node in social networks.

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