2010 ModelingRelationalEventsviaLate

• (DuBois et al., 2010) ⇒ Christopher DuBois, and Padhraic Smyth. (2010). “Modeling Relational Events via Latent Classes.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835906

Subject Headings:

Notes

• Categories and Subject Descriptors: I.5.1 Computing Methodologies: Pattern Recognition — Statistical Models
• General Terms: Algorithms; Experimentation

Cited By

• http://scholar.google.com/scholar?q=%22Modeling+relational+events+via+latent+classes%22+2010
• http://portal.acm.org/citation.cfm?id=1835906&preflayout=flat#citedby

Quotes

Author Keywords

Relational data, collapsed Gibbs sampling

Abstract

Many social networks can be characterized by a sequence of dyadic interactions between individuals. Techniques for analyzing such events are of increasing interest. In this paper, we describe a generative model for dyadic events, where each event arises from one of [math]\displaystyle{ C }[/math] latent classes, and the properties of the event (sender, recipient, and type) are chosen from distributions over these entities conditioned on the chosen class. We present two algorithms for inference in this model: an expectation-maximization algorithm as well as a Markov chain Monte Carlo procedure based on collapsed Gibbs sampling. To analyze the model's predictive accuracy, the algorithms are applied to multiple real-world data sets involving email communication, international political events, and animal behavior data.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 ModelingRelationalEventsviaLate	Padhraic Smyth Christopher DuBois			Modeling Relational Events via Latent Classes		KDD-2010 Proceedings		10.1145/1835804.1835906		2010