Topic modeling with network regularization
Topic modeling with network regularization
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DOI：
10.1145/1367497.1367512
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发表时间：
2008-04
期刊：
影响因子：
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通讯作者：
Qiaozhu Mei;Deng Cai;Duo Zhang;ChengXiang Zhai
中科院分区：
文献类型：
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作者：
Qiaozhu Mei;Deng Cai;Duo Zhang;ChengXiang Zhai
在本文中，我们形式化地定义了主题建模与网络结构（TMN）的问题。我们提出了一种新的解决方案，该方案基于数据中的图形结构，使用谐波正则化器正则化统计主题模型。所提出的方法将主题建模和社交网络分析联系起来，利用了统计主题模型和离散正则化的功能。该模型的输出很好地总结了文本中的主题，映射了网络上的主题，并发现了主题社区。通过对主题模型和基于图的正则化器的具体选择，我们的模型可以应用于作者主题分析、社区发现和空间文本挖掘等文本挖掘问题。对两种不同类型数据的实验表明，该方法是有效的，它改进了面向文本的方法以及面向网络的方法。所提出的模型是通用的，它可以应用于任何文本集合的主题和相关的网络结构的混合物。
In this paper, we formally define the problem of topic modeling with network structure (TMN). We propose a novel solution to this problem, which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data. The proposed method bridges topic modeling and social network analysis, which leverages the power of both statistical topic models and discrete regularization. The output of this model well summarizes topics in text, maps a topic on the network, and discovers topical communities. With concrete selection of a topic model and a graph-based regularizer, our model can be applied to text mining problems such as author-topic analysis, community discovery, and spatial text mining. Empirical experiments on two different genres of data show that our approach is effective, which improves text-oriented methods as well as network-oriented methods. The proposed model is general; it can be applied to any text collections with a mixture of topics and an associated network structure.