A generalized Co-HITS algorithm and its application to bipartite graphs

Recently many data types arising from data mining and Web search applications can be modeled as bipartite graphs. Examples include queries and URLs in query logs, and authors and papers in scientific literature. However, one of the issues is that previous algorithms only consider the content and link information from one side of the bipartite graph. There is a lack of constraints to make sure the final relevance of the score propagation on the graph, as there are many noisy edges within the bipartite graph. In this paper, we propose a novel and general Co-HITS algorithm to incorporate the bipartite graph with the content information from both sides as well as the constraints of relevance. Moreover, we investigate the algorithm based on two frameworks, including the iterative and the regularization frameworks, and illustrate the generalized Co-HITS algorithm from different views. For the iterative framework, it contains HITS and personalized PageRank as special cases. In the regulariza...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Hongbo Deng, Michael R. Lyu, Irwin King
• Keywords: Bipartite Graph, Data Mining, KDD 2009, Regularization Framework, Regularization Frameworks