A topical link model for community discovery in textual interaction graph

Zheng, Guoan; Guo, Jinwen; Yang, Lichun; Xu, Shun; Bao, Shenghua; Su, Zhong; Han, Dingyi; Yu, Yong

doi:10.1145/1871437.1871686

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Probabilistic topic models [5,20] are important tools for discovering the latent semantic patterns in various data including text [11,13], users [12,24] and movie ratings [18]. A major limitation of these basic topic models and many of their extensions is that they discover topics in flat structures without organizing them into groups or hierarchies.…”

Section: Introductionmentioning

confidence: 99%

Modeling topic hierarchies with the recursive chinese restaurant process

Kim

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Topic models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet processes (HDP) are simple solutions to discover topics from a set of unannotated documents. While they are simple and popular, a major shortcoming of LDA and HDP is that they do not organize the topics into a hierarchical structure which is naturally found in many datasets. We introduce the recursive Chinese restaurant process (rCRP) and a nonparametric topic model with rCRP as a prior for discovering a hierarchical topic structure with unbounded depth and width. Unlike previous models for discovering topic hierarchies, rCRP allows the documents to be generated from a mixture over the entire set of topics in the hierarchy. We apply rCRP to a corpus of New York Times articles, a dataset of MovieLens ratings, and a set of Wikipedia articles and show the discovered topic hierarchies. We compare the predictive power of rCRP with LDA, HDP, and nested Chinese restaurant process (nCRP) using heldout likelihood to show that rCRP outperforms the others. We suggest two metrics that quantify the characteristics of a topic hierarchy to compare the discovered topic hierarchies of rCRP and nCRP. The results show that rCRP discovers a hierarchy in which the topics become more specialized toward the leaves, and topics in the immediate family exhibit more affinity than topics beyond the immediate family.

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Section: Introductionmentioning

confidence: 99%

Modeling topic hierarchies with the recursive chinese restaurant process

Kim

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Latent Community Topic Analysis

Yin

Cao

et al. 2012

ACM Trans. Intell. Syst. Technol.

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This article studies the problem of latent community topic analysis in text-associated graphs. With the development of social media, a lot of user-generated content is available with user networks. Along with rich information in networks, user graphs can be extended with text information associated with nodes. Topic modeling is a classic problem in text mining and it is interesting to discover the latent topics in text-associated graphs. Different from traditional topic modeling methods considering links, we incorporate community discovery into topic analysis in text-associated graphs to guarantee the topical coherence in the communities so that users in the same community are closely linked to each other and share common latent topics. We handle topic modeling and community discovery in the same framework. In our model we separate the concepts of community and topic, so one community can correspond to multiple topics and multiple communities can share the same topic. We compare different methods and perform extensive experiments on two real datasets. The results confirm our hypothesis that topics could help understand community structure, while community structure could help model topics.

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A topical link model for community discovery in textual interaction graph

Cited by 2 publications

References 7 publications

Modeling topic hierarchies with the recursive chinese restaurant process

Modeling topic hierarchies with the recursive chinese restaurant process

Latent Community Topic Analysis

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