摘要
LeaderRank算法是一种高效的网络节点排名算法,已在相关领域得到了有效应用。本文基于LeaderRank提出一种新的核心节点识别算法,并给出Python实现方案。根据节点的影响力选择一些节点作为候选核心节点,若候选核心节点所在的社区满足弱社区定义,则该候选核心节点为核心节点。在人工网络和karate网络上的实验表明,本文算法可以较好地识别出核心节点和对应社区。
LeaderRank algorithm is an efficient network node ranking algorithm,which has been applied effectively in related research.This paper proposes a new core node recognition algorithm based on LeaderRank,and gives a Python implementation scheme.According to the influence of the nodes,some nodes are selected as candidate core nodes.The community where the candidate core node is located meets the definition of a weak community,then the candidate core node is core node.Experiments on artificial networks and karate networks show that the proposed algorithm can better identify core nodes and communities.
作者
吴清寿
罗远华
芦佳雄
WU Qing-shou;LUO Yuan-hua;LU Jia-xiong(College of Mathematics and Computer Science,Wuyi University,Wuyishan 354300,China;The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions(Wuyi University),Wuyishan 354300,China)
出处
《长春师范大学学报》
2020年第4期42-47,共6页
Journal of Changchun Normal University
基金
福建省自然科学基金项目“基于网络表征学习的动态重叠社区的实时发现”(2019J01835)
福建省中青年教师教育科研项目“同构动态社交网络中重叠与层次社区的实时发现”(JAT170608)
福建省高校重点实验室开放课题基金资助项目“基于网络表征学习的层次与重叠社区的实时发现”(KLCCIIP2018107)
福建省大学生创新创业训练计划项目“动态社交网络中的重叠社区的实时发现”(201810397044)。
