Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Stochastic link and group detection
Eighteenth national conference on Artificial intelligence
The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-way distributional clustering via pairwise interactions
ICML '05 Proceedings of the 22nd international conference on Machine learning
A latent mixed membership model for relational data
Proceedings of the 3rd international workshop on Link discovery
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Cross-cultural analysis of blogs and forums with mixed-collection topic models
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning
Combining stochastic block models and mixed membership for statistical network analysis
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Statistical models of music-listening sessions in social media
Proceedings of the 19th international conference on World wide web
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Modeling the evolution of discussion topics and communication to improve relational classification
Proceedings of the First Workshop on Social Media Analytics
Mining topics on participations for community discovery
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
We present a probabilistic generative model of entity relationships and textual attributes; the model simultaneously discovers groups among the entities and topics among the corresponding text. Block models of relationship data have been studied in social network analysis for some time, however here we cluster in multiple modalities at once. Significantly, joint inference allows the discovery of groups to be guided by the emerging topics, and vice-versa. We present experimental results on two large data sets: sixteen years of bills put before the U.S. Senate, comprising their corresponding text and voting records, and 43 years of similar data from the United Nations. We show that in comparison with traditional, separate latent-variable models for words or block structures for votes, our Group-Topic model's joint inference improves both the groups and topics discovered. Additionally, we present a non-Markov continouous-time group model to capture shifting group structure over time.