2015 ProbabilisticCommunityandRoleMo

• (Han & Tang, 2015) ⇒ Yu Han, and Jie Tang. (2015). “Probabilistic Community and Role Model for Social Networks.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783274

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Probabilistic+Community+and+Role+Model+for+Social+Networks
• http://dl.acm.org/citation.cfm?id=2783258.2783274&preflayout=flat#citedby

Quotes

Author Keywords

• Behavior prediction; community; data mining; social network; sociology

Abstract

Numerous models have been proposed for modeling social networks to explore their structure or to address application problems, such as community detection and behavior prediction. However, the results are still far from satisfactory. One of the biggest challenges is how to capture all the information of a social network in a unified manner, such as links, communities, user attributes, roles and behaviors.

In this paper, we propose a unified probabilistic framework, the Community Role Model (CRM), to model a social network. CRM incorporates all the information of nodes and edges that form a social network. We propose methods based on Gibbs sampling and an EM algorithm to estimate the model's parameters and fit our model to real social networks. Real data experiments show that CRM can be used not only to represent a social network, but also to handle various application problems with better performance than a baseline model, without any modification to the model, showing its great advantages.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 ProbabilisticCommunityandRoleMo	Jie Tang Yu Han			Probabilistic Community and Role Model for Social Networks				10.1145/2783258.2783274		2015