A New Adaptive Robust Modularized Semi-Supervised Community Detection Method Based on Non-negative Matrix Factorization

IF 2.6 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Processing Letters Pub Date : 2024-04-02 DOI:10.1007/s11063-024-11588-y

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Abstract

The most extensively used tools for categorizing complicated networks are community detection methods. One of the most common methods for unsupervised and semi-supervised clustering is community detection based on Non-negative Matrix Factorization (NMF). Nonetheless, this approach encounters multiple challenges, including the lack of specificity for the data type and the decreased efficiency when errors occur in each cluster’s knowledge priority. As modularity is the basic and thorough criterion for evaluating and validating performance of community detection methods, this paper proposes a new approach for modularity-based community detection which is similar to symmetric NMF. The provided approach is a semi-supervised adaptive robust community detection model referred to as modularized robust semi-supervised adaptive symmetric NMF (MRASNMF). In this model, the modularity criterion has been successfully combined with the NMF model via a novel multi-view clustering method. Also, the tuning parameter is adjusted iteratively via an adaptive method. MRASNMF makes use of knowledge priority, modularity criterion, reinforcement of non-negative matrix factorization, and has iterative solution, as well. In this regard, the MRASNMF model was evaluated and validated using five real-world networks in comparison to existing semi-supervised community detection approaches. According to the findings of this study, the proposed strategy is most effective for all types of networks.

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基于非负矩阵因式分解的新型自适应鲁棒模块化半监督社群检测方法

摘要用于对复杂网络进行分类的最广泛工具是社群检测方法。无监督和半监督聚类最常用的方法之一是基于非负矩阵因式分解（NMF）的群落检测。然而，这种方法遇到了多重挑战，包括数据类型缺乏特异性，以及当每个聚类的知识优先级出现错误时效率降低。由于模块性是评估和验证群落检测方法性能的基本而全面的标准，本文提出了一种与对称 NMF 相似的基于模块性的群落检测新方法。所提供的方法是一种半监督自适应鲁棒社区检测模型，称为模块化鲁棒半监督自适应对称 NMF（MRASNMF）。在该模型中，模块化准则通过一种新颖的多视角聚类方法成功地与 NMF 模型相结合。此外，还通过自适应方法对调整参数进行迭代调整。MRASNMF 利用了知识优先权、模块化准则、非负矩阵因式分解的强化，并具有迭代求解功能。为此，我们使用五个真实世界的网络对 MRASNMF 模型进行了评估和验证，并与现有的半监督群落检测方法进行了比较。研究结果表明，所提出的策略对所有类型的网络都最为有效。

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来源期刊

Neural Processing Letters 工程技术-计算机：人工智能

CiteScore

4.90

自引率

12.90%

发文量

392

审稿时长

2.8 months

期刊介绍： Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches. The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters