基于交叉图成对学习的稀疏域用户社区检测
Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning
摘要：
赛博空间承载着用户与不同类型对象之间的大量交互，它们之间的关系通常被封装为二分图。在这种异构图中检测用户社区是发现用户信息需求并进一步提高推荐性能的重要任务。虽然几个主要的网络域携带高质量的图形，不幸的是，大多数其他人可以相当稀疏。然而，由于用户可能出现在多个域（图）中，因此他们在主域中的高质量活动可以在稀疏域中提供社区发现，例如，Google上的用户行为可以帮助成千上万的应用程序在使用Google ID登录这些应用程序时定位他/她的本地社区。在本文中，我们的模型，成对交叉图社区发现（PCCD），提出了应对稀疏图的问题，涉及外部图的知识来学习用户成对社区的接近度，而不是检测直接社区。特别是在我们的模型中，为了避免获取过多的传播信息，使用了两级过滤模块，通过社区和节点级别的过滤器来选择信息量最大的连接。随后，社区递归单元（CRU）的设计，以估计成对用户社区的亲密度。在两个真实世界的图数据集上进行的大量实验验证了我们的模型对几个强大的替代品。补充实验也验证了它的鲁棒性与不同的稀疏度的图。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
