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Community Detection in Partially Observable Social Networks
arXiv - CS - Social and Information Networks Pub Date : 2017-12-30 , DOI: arxiv-1801.00132
Cong Tran, Won-Yong Shin, Andreas Spitz

The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks' topology and functions. However, most social network data are collected from partially observable networks with both missing nodes and edges. In this paper, we address a new problem of detecting overlapping community structures in the context of such an incomplete network, where communities in the network are allowed to overlap since nodes belong to multiple communities at once. To solve this problem, we introduce KroMFac, a new framework that conducts community detection via regularized nonnegative matrix factorization (NMF) based on the Kronecker graph model. Specifically, from an interred Kronecker generative parameter metrix, we first estimate the missing part of the network. As our major contribution to the proposed framework, to improve community detection accuracy, we then characterize and select influential nodes (which tend to have high degrees) by ranking, and add them to the existing graph. Finally, we uncover the community structures by solving the regularized NMF-aided optimization problem in terms of maximizing the likelihood of the underlying graph. Furthermore, adopting normalized mutual information (NMI), we empirically show superiority of our KroMFac approach over two baseline schemes by using both synthetic and real-world networks.

中文翻译:

部分可观察社交网络中的社区检测

社交网络中社区结构的发现已经引起了极大的关注,因为它是理解网络拓扑和功能的基本问题。然而,大多数社交网络数据都是从部分可观察的网络中收集的,同时缺少节点和边。在本文中,我们解决了在这种不完整网络的背景下检测重叠社区结构的新问题,其中允许网络中的社区重叠,因为节点一次属于多个社区。为了解决这个问题,我们引入了 KroMFac,一个新的框架,它通过基于 Kronecker 图模型的正则化非负矩阵分解 (NMF) 进行社区检测。具体来说,从一个 interred Kronecker 生成参数矩阵,我们首先估计网络的缺失部分。作为我们对提出的框架的主要贡献,为了提高社区检测的准确性,我们然后通过排名表征和选择有影响的节点(往往具有高度),并将它们添加到现有图中。最后,我们通过解决正则化 NMF 辅助优化问题,以最大化底层图的可能性来揭示社区结构。此外,采用归一化互信息 (NMI),我们通过使用合成和现实世界的网络,凭经验证明了我们的 KroMFac 方法在两个基线方案上的优越性。我们通过解决正则化的 NMF 辅助优化问题以最大化底层图的可能性来揭示社区结构。此外,采用归一化互信息 (NMI),我们通过使用合成和现实世界的网络,凭经验证明了我们的 KroMFac 方法在两个基线方案上的优越性。我们通过解决正则化的 NMF 辅助优化问题以最大化底层图的可能性来揭示社区结构。此外,采用归一化互信息 (NMI),我们通过使用合成和现实世界的网络,凭经验证明了我们的 KroMFac 方法在两个基线方案上的优越性。
更新日期:2020-04-21
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