Topic aspect analysis for multi-document summarization
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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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.