Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2018

Abstract

Determining the similarity between two objects is pertinent to many applications. When the basis for similarity is a set of object-to-object relationships, it is natural to rely on graph-theoretic measures. One seminal technique for measuring the structural-context similarity between a pair of graph vertices is SimRank, whose underlying intuition is that two objects are similar if they are connected by similar objects. However, by design, SimRank as well as its variants capture only a single view or perspective of similarity. Meanwhile, in many real-world scenarios, there emerge multiple perspectives of similarity, i.e., two objects may be similar from one perspective, but dissimilar from another. For instance, human subjects may generate varied, yet valid, clusterings of objects. In this work, we propose a graph-theoretic similarity measure that is natively multiperspective. In our approach, the observed object-to-object relationships due to various perspectives are integrated into a unified graph-based representation, stylised as a hypergraph to retain the distinct perspectives. We then introduce a novel model for learning and reflecting diverse similarity perceptions given the hypergraph, yielding the similarity score between any pair of objects from any perspective. In addition to proposing an algorithm for computing the similarity scores, we also provide theoretical guarantees on the convergence of the algorithm. Experiments on public datasets show that the proposed model deals better with multiperspectivity than the baselines.

Keywords

graph similarity, multiperspective, similarity learning

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

CIKM 2018: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, October 22-26

First Page

1223

Last Page

1232

ISBN

9781450360142

Identifier

10.1145/3269206.3271758

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3269206.3271758

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