Spatial compactness meets topical consistency: jointly modeling links and content for community detection

Authors:
Mrinmaya Sachan;Avinava Dubey;Shashank Srivastava;Eric P. Xing;Eduard Hovy
Affiliations:
Carnegie Mellon University, PITTSBURGH, PA, USA;Carnegie Mellon University, PITTSBURGH, PA, USA;Carnegie Mellon University, PITTSBURGH, PA, USA;Carnegie Mellon University, PITTSBURGH, PA, USA;Carnegie Mellon University, PITTSBURGH, PA, USA
Venue:
Proceedings of the 7th ACM international conference on Web search and data mining
Year:
2014

Citing 15
Cited 0

Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organization and Identification of Web Communities

Computer
Latent dirichlet allocation

The Journal of Machine Learning Research
The author-topic model for authors and documents

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Probabilistic models for discovering e-communities

Proceedings of the 15th international conference on World Wide Web
Joint latent topic models for text and citations

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixed Membership Stochastic Blockmodels

The Journal of Machine Learning Research
Topic-link LDA: joint models of topic and author community

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Evaluation methods for topic models

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Topic and role discovery in social networks

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Distributed Algorithms for Topic Models

The Journal of Machine Learning Research
It's who you know: graph mining using recursive structural features

Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Using content and interactions for discovering communities in social networks

Proceedings of the 21st international conference on World Wide Web
Using community information to improve the precision of link prediction methods

Proceedings of the 21st international conference companion on World Wide Web
A survey of fuzzy clustering

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we address the problem of discovering topically meaningful, yet compact (densely connected) communities in a social network. Assuming the social network to be an integer-weighted graph (where the weights can be intuitively defined as the number of common friends, followers, documents exchanged, etc.), we transform the social network to a more efficient representation. In this new representation, each user is a bag of her one-hop neighbors. We propose a mixed-membership model to identify compact communities using this transformation. Next, we augment the representation and the model to incorporate user-content information imposing topical consistency in the communities. In our model a user can belong to multiple communities and a community can participate in multiple topics. This allows us to discover community memberships as well as community and user interests. Our method outperforms other well known baselines on two real-world social networks. Finally, we also provide a fast, parallel approximation of the same.