PathSelClus
Abstract
Real-world, multiple-typed objects are often interconnected, forming heterogeneous information networks. A major challenge for link-based clustering in such networks is their potential to generate many different results, carrying rather diverse semantic meanings. In order to generate desired clustering, we propose to use meta-path , a path that connects object types via a sequence of relations, to control clustering with distinct semantics. Nevertheless, it is easier for a user to provide a few examples (seeds) than a weighted combination of sophisticated meta-paths to specify her clustering preference. Thus, we propose to integrate meta-path selection with user-guided clustering to cluster objects in networks, where a user first provides a small set of object seeds for each cluster as guidance. Then the system learns the weight for each meta-path that is consistent with the clustering result implied by the guidance, and generates clusters under the learned weights of meta-paths. A probabilistic approach is proposed to solve the problem, and an effective and efficient iterative algorithm, PathSelClus , is proposed to learn the model, where the clustering quality and the meta-path weights mutually enhance each other. Our experiments with several clustering tasks in two real networks and one synthetic network demonstrate the power of the algorithm in comparison with the baselines.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 01, 2013
- Source ID
- 10.1145/2500492
Entities
People
- Brandon Norick
- Jiawei Han
- Philip S. Yu
- Xiao Yu
- Xifeng Yan
- Yizhou Sun
Organizations
- Air Force Office of Scientific Research
- Division of Information and Intelligent Systems
- Engineering and Physical Sciences Research Council
- United States Army Research Laboratory
- University of California, Santa Barbara
- University of Illinois Urbana–Champaign
- University of Illinois at Chicago