Forward backward similarity search in knowledge networks

Similarity search is a fundamental problem in social and knowledge networks like GitHub, DBLP, Wikipedia, etc. Existing network similarity measures are limited because they only consider similarity from the perspective of the query node. However, due to the complicated topology of real-world network...

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Bibliographic Details
Published in:Knowledge-based systems 2017-03, Vol.119, p.20-31
Main Authors: Shi, Baoxu, Yang, Lin, Weninger, Tim
Format: Article
Language:English
Subjects:
Algorithms
Expert systems
Graph search
Knowledge graph
Knowledge management
Network topologies
Networks
Queries
Search algorithms
Semantics
Similarity
Similarity measures
Social networks
Topology
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Summary:Similarity search is a fundamental problem in social and knowledge networks like GitHub, DBLP, Wikipedia, etc. Existing network similarity measures are limited because they only consider similarity from the perspective of the query node. However, due to the complicated topology of real-world networks, ignoring the preferences of target nodes often results in odd or unintuitive performance. In this work, we propose a dual perspective similarity metric called Forward Backward Similarity (FBS) that efficiently computes topological similarity from the perspective of both the query node and the perspective of candidate nodes. The effectiveness of our method is evaluated by traditional quantitative ranking metrics and large-scale human judgement on four large real world networks. The proposed method matches human preference and outperforms other similarity search algorithms on community overlap and link prediction. Finally, we demonstrate top-5 rankings for five famous researchers on an academic collaboration network to illustrate how our approach captures semantics more intuitively than other approaches.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2016.11.025