Exploiting the Block Structure of Link Graph for Efficient Similarity Computation

Authors:
Pei Li;Yuanzhe Cai;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:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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Abstract

In many real-world domains, link graph is one of the most effective ways to model the relationships between objects. Measuring the similarity of objects in a link graph is studied by many researchers, but an effective and efficient method is still expected. Based on our observation of link graphs from real domains, we find the block structure naturally exists. We propose an algorithm called BlockSimRank , which partitions the link graph into blocks, and obtains similarity of each node-pair in the graph efficiently. Our method is based on random walk on two-layer model, with time complexity as low as O (n 4/3) and less memory need. Experiments show that the accuracy of BlockSimRank is acceptable when the time cost is the lowest.