2008 TopicModelingWithNetworkRegularization

• (Mei et al., 2008) ⇒ Qiaozhu Mei, Deng Cai, Duo Zhang, ChengXiang Zhai. (2008). “Topic Modeling with Network Regularization.” In: Proceedings of (WWW 2008). doi:10.1145/1367497.1367512

Subject Headings: Topic Modeling with Network Structure Task, Topic Modeling with Network Regularization Algorithm.

Cited by

• 51 http://scholar.google.com/scholar?cites=7676679068256526340

Quotes

Abstract

• 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.

2 Problem Formulation

• Based on the definitions of these concepts, we can formalize the major tasks of topic modeling with network structure (TMN) as follows:
  • Task 1: (Topic Extraction) Given a collection C and a network structure G, the task of Topic Extraction is to model and extract k major topic models, {µ1, ..., µk}, where k is a user specified parameter.
  • Task 2: (Topic Map Extraction) Given a collection C and a network structure G, the task of Topic Map Extraction is to model and extract the k weight vectors {Mµ1, ...,Mµk}, where each vector Mµ is a map of topic µ on network G.
  • Task 3: (Topical Community Discovery) Given a collection C and a network structure G, the task of Topical Community Discovery is to extract k topical communities {V1, ..., Vk}, where each Vi has a coherent semantic summary µi, which is one of the k major topics in C.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 TopicModelingWithNetworkRegularization	Qiaozhu Mei Deng Cai Duo Zhang ChengXiang Zhai			Topic Modeling with Network Regularization			http://wwwconference.org/www2008/papers/pdf/p101-meiA.pdf	10.1145/1367497.1367512