DepRank: A Probabilistic Measure of Dependence via Heterogeneous Links

Authors:
Pei Li;Bo Hu;Hongyan Liu;Jun He;Xiaoyong Du
Affiliations:
Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Department of Management Science and Engineering, Tsinghua University, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering, Ministry of Education, China and School of Information, Renmin University of China, Beijing, China
Venue:
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Year:
2009

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Abstract

Dependence is a common relationship between objects. Many works have paid their attentions on dependence, but many of them mainly focus on constructing or exploiting dependence graphs on some specific domain. In this paper, we give a generic definition of dependence and make equivalence to the situation of graphs, and propose an algorithm called DepRank, which quantifies the dependence degree of a node pair based on probabilities of all dependence paths. Empirical study shows that DepRank method has reasonable results and maintains a well balance between accuracy and efficiency.