Label Propagation on K-partite Graphs

Label propagation is an approach to assign class labels to unlabeled data given some partially labeled data. In this paper, we systematically generalize the Laplacian matrix based label propagation method from pairwise graph data to data objects described by bipartite and general K-partite graphs. By deriving explicit label propagation formula, we show how information on one type of variables can be transformed to other types of variables. For example, in a word-document-author multi-relational dataset, information on words and on authors can effectively enhance the document labeling. Motivating examples are presented to illustrate these new concepts. Extensive experiments are performed on real-life datasets to show the effectiveness of our label propagation.

[1]  Zhang Changshui,et al.  Reply networks on a bulletin board system. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Saso Dzeroski,et al.  Multi-relational data mining: an introduction , 2003, SKDD.

[3]  Chris H. Q. Ding,et al.  A learning framework using Green's function and kernel regularization with application to recommender system , 2007, KDD '07.

[4]  Jianying Hu,et al.  Regularized Co-Clustering with Dual Supervision , 2008, NIPS.

[5]  Thorsten Joachims,et al.  Transductive Learning via Spectral Graph Partitioning , 2003, ICML.

[6]  Philip S. Yu,et al.  Unsupervised learning on k-partite graphs , 2006, KDD '06.

[7]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..

[8]  John D. Lafferty,et al.  Semi-supervised learning using randomized mincuts , 2004, ICML.

[9]  Chris H. Q. Ding,et al.  Knowledge transformation from word space to document space , 2008, SIGIR '08.

[10]  Bernhard Schölkopf,et al.  Cluster Kernels for Semi-Supervised Learning , 2002, NIPS.

[11]  Zoubin Ghahramani,et al.  Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning , 2004, NIPS.

[12]  Philip S. Yu,et al.  Efficient classification across multiple database relations: a CrossMine approach , 2006, IEEE Transactions on Knowledge and Data Engineering.

[13]  Mikhail Belkin,et al.  Semi-Supervised Learning on Riemannian Manifolds , 2004, Machine Learning.

[14]  Yoshua Bengio,et al.  Semi-supervised Learning by Entropy Minimization , 2004, CAP.

[15]  Ji Zhu,et al.  A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning , 2004, NIPS.

[16]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[17]  Chris H. Q. Ding,et al.  Orthogonal nonnegative matrix t-factorizations for clustering , 2006, KDD '06.

[18]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[19]  Ben Taskar,et al.  Learning Probabilistic Models of Relational Structure , 2001, ICML.

[20]  Tomaso A. Poggio,et al.  Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..