A Seed Growth Algorithm for Local Clustering in Complex Networks

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-09-19 DOI:10.1109/TNSE.2024.3463639

Feng-Sheng Tsai;Sheng-Yi Hsu;Mau-Hsiang Shih

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Abstract

A seed growth algorithm based on a local connectivity rule for cluster formation in complex networks is introduced. That accompanies with the cluster normalization algorithm, the parameter determination process, and the pseudocluster inference process, forming the coherent algorithms and formalizing the categories within the realm of the cluster space. The prime clusters can be extracted from the cluster space, so that the overlapping complexity of clusters is confined to the prime clusters. To decide unequivocally whether the coherent algorithms are efficient, we have to simulate on the overlapping stochastic block networks. Our simulation shows that the dice coefficient of the prime cluster corresponding to the overlapping target cluster is 0.978± 0.024 on average. It decodes the underlying meaning that the coherent algorithms can efficiently search out the prime clusters containing almost the same nodes as the overlapping clusters. It provides a firm foundation for a simulation on the Caenorhabditis elegans neuronal network, unraveling the neurons DD04, PDB, VA08, VB06, VB07, VD07, and VD08 lying in the major overlap of clusters, among them the ablation of DD04 and PDB in biological experiments has shown to result in a pronounced loss of controllability of motor behavior.

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复杂网络中局部聚类的种子增长算法

本文介绍了一种基于局部连通性规则的种子生长算法，用于在复杂网络中形成聚类。该算法与聚类归一化算法、参数确定过程和伪聚类推理过程相配合，形成了一致的算法，并在聚类空间领域内将类别形式化。可以从聚类空间中提取质簇，从而将聚类的重叠复杂性限制在质簇范围内。为了明确判定一致性算法是否有效，我们必须对重叠随机块网络进行仿真。模拟结果表明，与重叠目标簇相对应的质点簇的骰子系数平均为 0.978±0.024。这解释了相干算法可以高效地搜索出与重叠簇包含几乎相同节点的素数簇的内在含义。它为对秀丽隐杆线虫神经元网络的模拟提供了坚实的基础，揭示了位于主要重叠簇中的神经元 DD04、PDB、VA08、VB06、VB07、VD07 和 VD08，其中 DD04 和 PDB 在生物实验中被消融后会导致运动行为明显失去可控性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.