Deep Learning for Community Detection: Progress, Challenges and Opportunities
Deep Learning for Community Detection: Progress, Challenges and Opportunities
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DOI：
10.24963/ijcai.2020/693
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发表时间：
2020-05
期刊：
影响因子：
1.9
通讯作者：
中科院分区：
文献类型：
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由于社区代表着相似的观点、相似的功能、相似的目的等，在科学研究和数据分析中，社区检测是一个重要且极其有用的工具。然而，经典的社区检测方法，如谱聚类和统计推断，正在被淘汰，因为深度学习技术表现出越来越大的处理高维图数据的能力，性能令人印象深刻。因此，对通过深度学习进行社区检测的当前进展进行调查是及时的。本文分为该领域的三大研究流-深度神经网络，深度图嵌入和图神经网络，总结了每个流中各种框架，模型和算法的贡献沿着当前尚未解决的挑战和有待探索的未来研究机会。
As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain – deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.