Zheng Gao, Hongsong Li, Zhuoren Jiang, Xiaozhong Liu
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In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities. Particularly in our model, to avoid taking excessive propagated information, a two-level filtering module is utilized to select the most informative connections through both community and node level filters. Subsequently, a Community Recurrent Unit (CRU) is designed to estimate pairwise user community closeness. Extensive experiments on two real-world graph datasets validate our model against several strong alternatives. 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引用次数: 5
摘要
网络空间承载着用户与各种对象之间丰富的交互,它们之间的关系往往被封装为二部图。在这种异构图中检测用户社区是发现用户信息需求和进一步提高推荐性能的基本任务。虽然有几个主要的网络域携带高质量的图表,但不幸的是,大多数其他的网络域可能相当稀疏。然而,由于用户可能出现在多个域(图)中,他们在主域中的高质量活动可以在稀疏域中提供社区检测,例如,当用户使用Google ID登录这些应用程序时,用户在Google上的行为可以帮助成千上万的应用程序定位他/她的本地社区。在本文中,我们提出了PCCD (Pairwise Cross-graph Community Detection)模型来解决稀疏图问题,通过引入外部图知识来学习用户成对的社区亲密度,而不是直接检测社区。特别是在我们的模型中,为了避免获取过多的传播信息,我们使用了一个两级过滤模块,通过社区级和节点级过滤来选择信息量最大的连接。随后,设计了一个社区循环单元(CRU)来估计两两用户社区亲密度。在两个真实世界的图形数据集上进行的大量实验验证了我们的模型与几个强大的替代方案的对比。补充实验也验证了其对不同稀疏度尺度图的鲁棒性。
Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning
Cyberspace hosts abundant interactions between users and different kinds of objects, and their relations are often encapsulated as bipartite graphs. Detecting user community in such heterogeneous graphs is an essential task to uncover user information needs and to further enhance recommendation performance. While several main cyber domains carrying high-quality graphs, unfortunately, most others can be quite sparse. However, as users may appear in multiple domains (graphs), their high-quality activities in the main domains can supply community detection in the sparse ones, e.g., user behaviors on Google can help thousands of applications to locate his/her local community when s/he uses Google ID to login those applications. In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities. Particularly in our model, to avoid taking excessive propagated information, a two-level filtering module is utilized to select the most informative connections through both community and node level filters. Subsequently, a Community Recurrent Unit (CRU) is designed to estimate pairwise user community closeness. Extensive experiments on two real-world graph datasets validate our model against several strong alternatives. Supplementary experiments also validate its robustness on graphs with varied sparsity scales.