AsymSim: meta path-based similarity with asymmetric relations

Peer similarity search is a deceptively complex problem in information network analysis. Past research has primarily focused on similarity search in homogeneous networks, but real world data is often best represented using heterogeneous information networks, with multiple node and relation types carrying real-world semantics. Recent work addresses similarity search in heterogeneous networks by introducing the concept of meta paths, or paths that connect object types via a sequence of relations. These meta path-based similarity measures can capture the subtlety of peer similarity for paths containing symmetric edges, but real data contains asymmetric relations that play a significant role in peer similarity semantics, for instance citations in bibliographic networks. In this paper, we revisit the problem of peer similarity search among objects of the same type in heterogeneous information networks. We present an efficient meta path-based peer similarity measure, AsymSim, which both captures the semantics of peer similarity and remains sensitive to asymmetric relations in the network, allowing us to extract deeper peer semantics. We discuss how to efficiently handle AsymSim queries online and perform experiments on real DBLP data to verify the effectiveness of our proposed measure.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Tedesco, Jonathan

Contributors dc:contributor

• Han, Jiawei
• Heeren, Cinda

• Heterogeneous information networks
• similarity search

Statement dc:rights

• Copyright 2013 Jonathan Tedesco

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/44188

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/44188