Graph Clustering with Embedding Propagation
Graph Clustering with Embedding Propagation
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DOI：
10.1109/bigdata50022.2020.9378031
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Carl Yang;Liyuan Liu;Mengxiong Liu;Zongyi Wang;Chao Zhang;Jiawei Han
中科院分区：
文献类型：
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作者：
Carl Yang;Liyuan Liu;Mengxiong Liu;Zongyi Wang;Chao Zhang;Jiawei Han
在过去的十年中，属性网络数据的数量激增，识别其底层组结构的问题受到了极大的关注。通过利用属性和链接信息，最近最先进的网络聚类方法在相对干净的数据集上取得了显着的改进。然而，现实世界的属性网络的噪声性质长期以来一直被忽视，这导致性能下降，面临丢失或不准确的属性和链接。在这项工作中，我们通过结合聚类和嵌入属性网络的优势来克服这些弱点。具体来说，我们提出了GRACE（GRAph聚类与嵌入传播），同时学习网络表示和识别网络集群在一个端到端的方式。它采用深度去噪自动编码器从节点属性生成鲁棒的网络嵌入，在网络中传播嵌入以捕获节点交互，并基于嵌入传播的稳定状态检测集群。为了提供更多的洞察力，我们进一步分析GRACE在理论上的方式，并找到其与两个典型的网络建模方法的潜在联系。在六个真实世界的属性网络上进行的大量实验证明了GRACE在各种基线上的优越性。值得注意的是，GRACE将最强基线的平均性能从0.43提高到0.52，相对提高了21%。控制实验和案例研究进一步验证了我们的直觉，并展示了GRACE处理真实世界属性网络中噪声信息的能力。
In the past decade, the amount of attributed network data has skyrocketed, and the problem of identifying their underlying group structures has received significant attention. By leveraging both attribute and link information, recent state-of-the-art network clustering methods have achieved significant improvements on relatively clean datasets. However, the noisy nature of real-world attributed networks has long been over-looked, which leads to degraded performance facing missing or inaccurate attributes and links. In this work, we overcome such weaknesses by marrying the strengths of clustering and embedding on attributed networks. Specifically, we propose GRACE (GRAph Clustering with Embedding propagation), to simultaneously learn network representations and identify net-work clusters in an end-to-end manner. It employs deep denoise autoencoders to generate robust network embeddings from node attributes, propagates the embeddings in the network to capture node interactions, and detects clusters based on the stable state of embedding propagation. To provide more insight, we further analyze GRACE in a theoretical manner and find its underlying connections with two canonical approaches for network modeling. Extensive experiments on six real-world attributed networks demonstrate the superiority of GRACE over various baselines from the state-of-the-art. Remarkably, GRACE improves the averaged performance of the strongest baseline from 0.43 to 0.52, yielding a 21% relative improvement. Controlled experiments and case studies further verify our intuitions and demonstrate the ability of GRACE to handle noisy information in real-world attributed networks.