SimRank: a measure of structural-context similarity
G. Jeh, und J. Widom. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, Seite 538--543. New York, NY, USA, ACM, (2002)
DOI: 10.1145/775047.775126
Zusammenfassung
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 <i>SimRank</i>, 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.
%0 Conference Paper
%1 jeh2002simrank
%A Jeh, Glen
%A Widom, Jennifer
%B Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
%C New York, NY, USA
%D 2002
%I ACM
%K graph link matrix prediction similarity vertex
%P 538--543
%R 10.1145/775047.775126
%T SimRank: a measure of structural-context similarity
%U http://doi.acm.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 <i>SimRank</i>, 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{jeh2002simrank,
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 <i>SimRank</i>, 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.},
acmid = {775126},
added-at = {2012-06-22T10:25:31.000+0200},
address = {New York, NY, USA},
author = {Jeh, Glen and Widom, Jennifer},
biburl = {https://www.bibsonomy.org/bibtex/29c266f8089a41473e012c210e25f519e/folke},
booktitle = {Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining},
description = {SimRank},
doi = {10.1145/775047.775126},
interhash = {a6d4531690305dc44937118df813b4b5},
intrahash = {9c266f8089a41473e012c210e25f519e},
isbn = {1-58113-567-X},
keywords = {graph link matrix prediction similarity vertex},
location = {Edmonton, Alberta, Canada},
numpages = {6},
pages = {538--543},
publisher = {ACM},
series = {KDD '02},
timestamp = {2012-06-22T10:25:31.000+0200},
title = {SimRank: a measure of structural-context similarity},
url = {http://doi.acm.org/10.1145/775047.775126},
year = 2002
}