2010
NIPS
NeurIPS 2010
Dynamic Infinite Relational Model for Time-varying Relational Data Analysis
Abstract
We propose a new probabilistic model for analyzing dynamic evolutions of relational data, such as additions, deletions and split & merge, of relation clusters like communities in social networks. Our proposed model abstracts observed time-varying object-object relationships into relationships between object clusters. We extend the infinite Hidden Markov model to follow dynamic and time-sensitive changes in the structure of the relational data and to estimate a number of clusters simultaneously. We show the usefulness of the model through experiments with synthetic and real-world data sets.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Keyword Pioneer
— community detection
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
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Trend Setter
— Online Learning
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Hot Topic Early Bird
— probabilistic modeling
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Clustering
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Stochastic Processes
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Learning Paradigms > Online Learning