title: Robust label propagation on multiple networks.
abstract: Transductive inference on graphs such as label propagation algorithms is receiving a lot of attention. In this paper, we address a label propagation problem on multiple networks and present a new algorithm that automatically integrates structure information brought in by multiple networks. The proposed method is robust in that irrelevant networks are automatically deemphasized, which is an advantage over Tsuda's approach (2005). We also show that the proposed algorithm can be interpreted as an expectation-maximization (EM) algorithm with a student-t prior. Finally, we demonstrate the usefulness of our method in protein function prediction and digit classification, and show analytically and experimentally that our algorithm is much more efficient than existing algorithms.
authors.affiliation: Center for Informational Biology, Ochanomizu University, Tokyo 112-8610, Japan. kato-tsuyoshi@aist.go.jp
authors.forename: Tsuyoshi
authors.initials: T
authors.lastname: Kato
authors.forename: Hisahi
authors.initials: H
authors.lastname: Kashima
authors.forename: Masashi
authors.initials: M
authors.lastname: Sugiyama
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011336:Probability; D011506:Proteins / Q000737:chemistry; D012372:ROC Curve; D013329:Structure-Activity Relationship; D013997:Time Factors
chemical_list: D011506:Proteins