%0 Conference Paper %1 citeulike:349900 %A Jeh, Glen %A Widom, Jennifer %B KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining %C New York, NY, USA %D 2002 %I ACM %K clustering, web-graph %P 538--543 %R 10.1145/775047.775126 %T SimRank: a measure of structural-context similarity %U http://dx.doi.org/10.1145/775047.775126 %X The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects:" This general similarity measure, called SimRank , is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach. %@ 1-58113-567-X

@inproceedings{citeulike:349900, abstract = {The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects:" This general similarity measure, called SimRank , is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.}, added-at = {2009-08-06T15:16:38.000+0200}, address = {New York, NY, USA}, author = {Jeh, Glen and Widom, Jennifer}, biburl = {https://www.bibsonomy.org/bibtex/29b901eee9786a06b6ef7203e7a920608/chato}, booktitle = {KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {349900}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=775126}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/775047.775126}, doi = {10.1145/775047.775126}, interhash = {a6d4531690305dc44937118df813b4b5}, intrahash = {9b901eee9786a06b6ef7203e7a920608}, isbn = {1-58113-567-X}, keywords = {clustering, web-graph}, location = {Edmonton, Alberta, Canada}, pages = {538--543}, posted-at = {2006-06-05 15:49:45}, priority = {0}, publisher = {ACM}, timestamp = {2009-08-06T15:16:52.000+0200}, title = {SimRank: a measure of structural-context similarity}, url = {http://dx.doi.org/10.1145/775047.775126}, year = 2002 }