Node similarity in the citation graph

Authors:
Wangzhong Lu;J. Janssen;E. Milios;N. Japkowicz;Yongzheng Zhang
Affiliations:
Faculty of Computer Science, Dalhousie University, 6050 University Ave., B3H 1W5, Halifax, Nova Scotia, Canada;Dalhousie University, Department of Mathematics and Statistics, 6050 University Ave., B3H 3J5, Halifax, Nova Scotia, Canada;Faculty of Computer Science, Dalhousie University, 6050 University Ave., B3H 1W5, Halifax, Nova Scotia, Canada;University of Ottawa, School of Information Technology and Engineering, 6050 University Ave., K1N 6N5, Ottawa, Ontario, Canada;Faculty of Computer Science, Dalhousie University, 6050 University Ave., B3H 1W5, Halifax, Nova Scotia, Canada
Venue:
Knowledge and Information Systems
Year:
2006

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Abstract

Published scientific articles are linked together into a graph, the citation graph, through their citations. This paper explores the notion of similarity based on connectivity alone, and proposes several algorithms to quantify it. Our metrics take advantage of the local neighborhoods of the nodes in the citation graph. Two variants of link-based similarity estimation between two nodes are described, one based on the separate local neighborhoods of the nodes, and another based on the joint local neighborhood expanded from both nodes at the same time. The algorithms are implemented and evaluated on a subgraph of the citation graph of computer science in a retrieval context. The results are compared with text-based similarity, and demonstrate the complementarity of link-based and text-based retrieval.