CLARE: A Semi-supervised Community Detection Algorithm
CLARE: A Semi-supervised Community Detection Algorithm
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DOI：
10.1145/3534678.3539370
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Xixi Wu;Yun Xiong;Yao Zhang;Yizhu Jiao;Caihua Shan;Yiheng Sun;Yangyong Zhu;P. Yu
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文献类型：
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作者：
Xixi Wu;Yun Xiong;Yao Zhang;Yizhu Jiao;Caihua Shan;Yiheng Sun;Yangyong Zhu;P. Yu
社区检测是指发现密切相关的子图以理解网络的任务。然而，传统的社区检测算法无法精确定位特定类型的社区。这限制了它在现实网络中的适用性，例如区分交易网络中的欺诈群体和正常群体。最近，半监督社区检测作为一种解决方案出现。它的目的是在网络中寻找其他类似的社区，而标记社区很少作为训练数据。现有的工作可以看作是基于种子的：定位种子节点，然后围绕种子发展社区。然而，这些方法对所选种子的质量非常敏感，因为围绕错误检测的种子生成的群落可能无关紧要。此外，它们还存在各自的问题，例如不灵活性和高计算开销。为了解决这些问题，我们提出了 CLARE，它由两个关键组件组成：社区定位器和社区重写器。我们的想法是，我们可以找到潜在的社区，然后完善它们。因此，提出了社区定位器，通过寻找与网络中的训练子图相似的子图来快速定位潜在的社区。为了进一步调整这些定位的社区，我们设计了社区重写器。通过深度强化学习的增强，它提出智能决策，例如添加或删除节点，以灵活地完善社区结构。与先前在多个现实世界数据集上最先进的方法相比，大量的实验验证了我们工作的有效性和效率。
Community detection refers to the task of discovering closely related subgraphs to understand the networks. However, traditional community detection algorithms fail to pinpoint a particular kind of community. This limits its applicability in real-world networks, e.g., distinguishing fraud groups from normal ones in transaction networks. Recently, semi-supervised community detection emerges as a solution. It aims to seek other similar communities in the network with few labeled communities as training data. Existing works can be regarded as seed-based: locate seed nodes and then develop communities around seeds. However, these methods are quite sensitive to the quality of selected seeds since communities generated around a mis-detected seed may be irrelevant. Besides, they have individual issues, e.g., inflexibility and high computational overhead. To address these issues, we propose CLARE, which consists of two key components, Community Locator and Community Rewriter. Our idea is that we can locate potential communities and then refine them. Therefore, the community locator is proposed for quickly locating potential communities by seeking subgraphs that are similar to training ones in the network. To further adjust these located communities, we devise the community rewriter. Enhanced by deep reinforcement learning, it suggests intelligent decisions, such as adding or dropping nodes, to refine community structures flexibly. Extensive experiments verify both the effectiveness and efficiency of our work compared with prior state-of-the-art approaches on multiple real-world datasets.