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Combining a popularity-productivity stochastic block model with a discriminative-content model for general structure
Bian-fang Chai1, Jian Yu, Cai-Yan Jia
1Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China.
Summary
This study introduces a new model for discovering communities in text-associated networks by considering both link structure and node content. The proposed model effectively identifies general community structures and improves performance on real-world networks.
Area of Science:
- Network Science
- Data Mining
- Machine Learning
Background:
- Latent community discovery in text-associated networks is crucial with social media's rise.
- Existing methods often fail to detect general structures like bipartite communities or account for node content.
- Stochastic block model variants lack node degree distribution modeling and content integration.
Purpose of the Study:
- To propose a novel model for latent community discovery in text-associated networks.
- To address limitations of existing methods in identifying general structures and incorporating node content.
- To develop a model that captures node differences in link reception (popularity) and production (productivity).
Main Methods:
- Introduction of the popularity-productivity stochastic block (PPSB) model.
- Integration of the PPSB model with a discriminative model for node content.
- Development of expectation-maximization (EM) algorithms for parameter inference.
Main Results:
- The proposed PPSB model effectively models scale-free networks with power-law degree distributions.
- The combined model successfully incorporates node content for community discovery.
- Experimental results show superior performance compared to previous models, especially on networks with general structures.
Conclusions:
- The proposed models offer a flexible and effective approach to latent community discovery in text-associated networks.
- The integration of popularity, productivity, and content significantly enhances community detection capabilities.
- The models provide a robust framework for analyzing complex network structures and node attributes.
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