Prediction of Protein Function Using Protein-Protein Interaction Data
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
The Journal of Machine Learning Research
The Journal of Machine Learning Research
ACM SIGKDD Explorations Newsletter
Recovering temporally rewiring networks: a model-based approach
Proceedings of the 24th international conference on Machine learning
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Scalable community discovery on textual data with relations
Proceedings of the 17th ACM conference on Information and knowledge management
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning
Joint group and topic discovery from relations and text
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
An efficient block model for clustering sparse graphs
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Social network mining with nonparametric relational models
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
| Hi-index | 0.00 |
Modeling relational data is an important problem for modern data analysis and machine learning. In this paper we propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way objects interact with one another in order to learn latent groups, their typical interaction patterns, and the degree of membership of objects to groups. Our model explains the data using a small set of parameters that can be reliably estimated with an efficient inference algorithm. In our approach, the set of probabilistic assumptions may be tailored to a specific application domain in order to incorporate intuitions and/or semantics of interest. We demonstrate our methods on simulated data and we successfully apply our model to a data set of protein-to-protein interactions.