社区检测的图重建与吸引方法

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Intelligence Pub Date : 2025-01-21 DOI:10.1007/s10489-024-05858-4
Xunlian Wu, Da Teng, Han Zhang, Jingqi Hu, Yining Quan, Qiguang Miao, Peng Gang Sun
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引用次数: 0

摘要

社区检测作为复杂网络中的热点问题之一,在过去的几十年里引起了人们的广泛关注。尽管许多方法在这个问题上表现良好,但如果网络表现出更复杂的特征,例如强重叠社区,它们就变得无能为力。本文探讨了一种用于社区检测的图重建和吸引方法。在GRAM中,我们通过引入一个新的基于马尔可夫链的通过概率矩阵来提取图的网络结构信息,利用该矩阵对新图进行重构,并在重构后的图上采用模块化优化代替原图进行无重叠社团检测。为了识别重叠社区,我们首先以重要节点作为吸引原点初始化集群,然后根据通过概率对集群进行图吸引扩展。对于剩余的节点重复此过程,并且每个孤立的节点(如果存在)最终被分类到其最吸引的集群中。在人工和现实数据集上的实验表明,该方法在社区检测方面具有优越性,特别是在具有更复杂、稀疏和模糊网络结构的数据集上。
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Graph reconstruction and attraction method for community detection

Community detection as one of the hot issues in complex networks has attracted a large amount of attention in the past several decades. Although many methods perform well on this problem, they become incapable if the networks exhibit more complicated characteristics, e.g. strongly overlapping communities. This paper explores a graph reconstruction and attraction method (GRAM) for community detection. In GRAM, we extract network structure information of a graph by introducing a new passing probability matrix based on Markov Chains by which a new graph is further reconstructed, and modularity optimization is adopted on the reconstructed one instead of the original one for non-overlapping community detection. For identifying overlapping communities, we first initialize a cluster with a vital node as an origin of attraction, then the cluster is extended by graph attraction based on the passing probability. This procedure is repeated for the remaining nodes, and each isolated node if exists is finally classified into its most attractable cluster. Experiments on artificial and real-world datasets have shown the superiority of the proposed method for community detection particularly on the datasets with even more complex, sparse and ambiguous network structures.

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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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