[论文解读] A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

Di Jin, Zhizhi Yu+6 more|arXiv (Cornell University)|Jan 3, 2021

Complex Network Analysis Techniques参考文献 160被引用 53

一句话总结

本综述提供了社区检测方法的统一视图，将其分为概率图模型和深度学习，并提供基准数据集和未来方向。它强调理论分析、分类法以及学习型方法的实际资源。

ABSTRACT

Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a unified architecture of network community-finding methods to characterize the state-of-the-art of the field of community detection. Specifically, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

研究动机与目标

• 提供学习型社区检测方法的统一概览。
• 引入两大类分类法：概率图模型和基于深度学习的方法。
• 分析方法之间的理论联系、挑战和差异。
• 发布基准数据集以促进社区检测领域的未来研究。
• 讨论现实世界的应用和未来的研究方向。

提出的方法

• 将现有方法分为两大类：概率图模型和深度学习。
• 详细说明概率模型中的子类别（有向、无向和混合）以及深度学习中的子类别（自编码器、生成对抗网络、图卷积网络，以及与图模型的混合）。
• 描述代表性模型和学习范式（如随机块模型、MMSB、主题模型、矩阵分解和图神经网络）。
• 提供学习型社区检测方法的统一体系结构视图。
• 发布基准数据集并讨论应用以推动进一步研究。

实验结果

研究问题

• RQ1 社区检测的主要学习型范式有哪些？它们在建模假设上有何差异？
• RQ2 如何将概率图模型与深度学习方法组织成一个统一的分类体系？
• RQ3 哪些理论见解和实际挑战表征学习型社区检测？
• RQ4 哪些基准资源和应用有助于推动该领域的未来研究？

主要发现

• 本文首次提供将学习型社区检测分为概率图模型和深度学习的全面综述。
• 它分析方法之间的相似性、差异和挑战，并提出未来工作的五个方向。
• 给出一个统一的系统体系结构，以将统计建模与深度学习方法联系起来。
• 跨领域的基准数据集和应用被发布，以支持未来的研究。
• 该综述讨论了SBM及相关方法的动态、重叠和链接社区扩展。
• 它强调深度学习（GCN、自编码器、GANs）在处理高维网络数据方面如何补充传统的概率建模。