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Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics

机译:利用链接语义,强大地检测大型社交网络中的链接社区

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Community detection has been extensively studied for various applications, focusing primarily on network topologies. Recent research has started to explore node contents to identify semantically meaningful communities and interpret their structures using selected words. However, links in real networks typically have semantic descriptions, e.g., comments and emails in social media, supporting the notion of communities of links. Indeed, communities of links can better describe multiple roles that nodes may play and provide a richer characterization of community behaviors than communities of nodes. The second issue in community finding is that most existing methods assume network topologies and descriptive contents to be consistent and to carry the compatible information of node group membership, which is generally violated in real networks. These methods are also restricted to interpret one community with one topic. The third problem is that the existing methods have used top ranked words or phrases to label topics when interpreting communities. However, it is often difficult to comprehend the derived topics using words or phrases, which may be irrelevant. To address these issues altogether, we propose a new unified probabilistic model that can be learned by a dual nested expectation-maximization algorithm. Our new method explores the intrinsic correlation between communities and topics to discover link communities robustly and extract adequate community summaries in sentences instead of words for topic labeling at the same time. It is able to derive more than one topical summary per community to provide rich explanations. We present experimental results to show the effectiveness of our new approach, and evaluate the quality of the results by a case study.
机译:社区检测已被广泛研究各种应用,主要关注网络拓扑。最近的研究已开始探索节点内容,以识别语义有意义的社区,并使用所选单词解释其结构。然而,真实网络中的链接通常具有语义描述,例如社交媒体中的评论和电子邮件,支持链接社区的概念。实际上,链接的社区可以更好地描述节点可以播放的多个角色,并且提供的社区行为比节点社区更丰富。社区发现中的第二个问题是,大多数现有方法都假定网络拓扑和描述性内容是一致的,并携带节点组成员资格的兼容信息,这通常在真实网络中违反。这些方法也仅限于用一个主题解释一个社区。第三个问题是现有方法在解释社区时使用了顶级排名的单词或短语来标记主题。然而,通常难以使​​用可能无关的单词或短语来理解导出的主题。为了完全解决这些问题,我们提出了一种新的统一概率模型,可以通过双嵌套期望 - 最大化算法学习。我们的新方法探讨了社区与主题之间的内在关联,以强大地发现链接社区,并在句子中提取足够的社区摘要而不是同时标签的主题标签。它能够派生多个局部摘要,以提供丰富的解释。我们提出了实验结果,以表明我们的新方法的有效性,并通过案例研究评估结果的质量。

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