Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
Multiplicative latent factor models for description and prediction of social networks
Computational & Mathematical Organization Theory
Infinite factorization of multiple non-parametric views
Machine Learning
Human interaction discovery in smartphone proximity networks
Personal and Ubiquitous Computing
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
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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 C 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.