2010
NeurIPS
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.
🌉
Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics
📈
Trend Setter
- Time Series Analysis
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Keyword Pioneer
- social network analysis
🐣
Hot Topic Early Bird
- hidden markov model
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Clustering
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Optimization & Theory > Stochastic Processes
Data Science & Analytics > Methods > Time Series Analysis
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Learning Types > Online Learning
Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian
Keywords
stochastic processes
nonparametric bayesian
probabilistic modeling
bayesian inference
cluster analysis
community detection
social network analysis
time series
relational data clustering
infinite hidden markov model
hidden markov model
probabilistic model
probabilistic graphical model
relational clustering
dynamic relational data
relational data