Combining link and content for community detection: a discriminative approach
Combining link and content for community detection: a discriminative approach
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DOI：
10.1145/1557019.1557120
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Tianbao Yang;Rong Jin;Yun Chi;Shenghuo Zhu
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文献类型：
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作者：
Tianbao Yang;Rong Jin;Yun Chi;Shenghuo Zhu
在本文中，我们考虑了链接和内容分析相结合的问题，用于从网络数据（如论文引文网络和万维网）中进行社区检测。大多数现有方法通过生成模型将链接和内容信息结合起来，生成模型通过一组共享的社区成员生成链接和内容。这些生成模型有一些缺点，因为它们没有考虑可能影响社区成员的其他因素，并且隔离了与社区成员无关的内容。为了明确地解决这些缺点，我们提出了一个将链接和内容分析相结合的社区检测判别模型。首先，我们提出了一个链接分析的条件模型，在该模型中，我们引入了隐变量来显式地模拟节点的流行程度。其次，为了减轻不相关内容属性的影响，我们开发了一个判别模型来进行内容分析。这两个模型通过社区成员无缝地统一起来。我们提出了基于界优化和交替投影的有效算法来解决相关的优化问题。对基准数据集的大量实验表明，所提出的框架明显优于将链接和内容分析结合起来进行社区检测的最先进方法。
In this paper, we consider the problem of combining link and content analysis for community detection from networked data, such as paper citation networks and Word Wide Web. Most existing approaches combine link and content information by a generative model that generates both links and contents via a shared set of community memberships. These generative models have some shortcomings in that they failed to consider additional factors that could affect the community memberships and isolate the contents that are irrelevant to community memberships. To explicitly address these shortcomings, we propose a discriminative model for combining the link and content analysis for community detection. First, we propose a conditional model for link analysis and in the model, we introduce hidden variables to explicitly model the popularity of nodes. Second, to alleviate the impact of irrelevant content attributes, we develop a discriminative model for content analysis. These two models are unified seamlessly via the community memberships. We present efficient algorithms to solve the related optimization problems based on bound optimization and alternating projection. Extensive experiments with benchmark data sets show that the proposed framework significantly outperforms the state-of-the-art approaches for combining link and content analysis for community detection.