A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in network analysis. Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages in handling high dimensional network data. Hence, a comprehensive overview of community detection's latest progress through deep learning is timely to academics and practitioners. This survey devises and proposes a new taxonomy covering different state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep sparse filtering. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders. The survey also summarizes the popular benchmark data sets, evaluation metrics, and open-source implementations to address experimentation settings. We then discuss the practical applications of community detection in various domains and point to implementation scenarios. Finally, we outline future directions by suggesting challenging topics in this fast-growing deep learning field.
翻译:一个社区揭示了其成员的特点和联系,这些特征和联系与其他社区在网络中的特征不同。在网络分析中,检测社区具有重大意义。尽管采用了传统的光谱集群和统计推断方法,但我们注意到近年来社区探测的深层学习技术有了显著发展,这些技术在处理高维网络数据方面具有优势。因此,对学术界和从业人员来说,通过深层次学习对社区探测的最新进展进行全面审查是及时的。这项调查设计并提出了一个新的分类,涵盖不同的最新方法,包括深层神经网络的深层学习模型、深非负矩阵化和深度稀少的过滤。主要类别,即深神经网络,被进一步划分为共进化网络、图示关注网络、突变对抗网络和自动组合。调查还总结了流行的基准数据集、评价指标和开源实施,以应对实验环境。我们随后讨论了社区探测在各个领域的实际应用情况,并提出了执行设想的要点。最后,我们通过提出这一迅速发展的深层次学习领域的富有挑战性的专题,概述了今后的方向。