TOP-K SIMILARITY JOIN IN HETEROGENEOUS INFORMATION NETWORKS

Volume 19 No 7 (2021)

Akash Chauhan

DOI: 10.48047/nq.2021.19.7.NQ21127

Abstract

A basic function in database and web search engines is similarity search. It is crucial to examine similarity search in such networks in light of the rise of large-scale heterogeneous information networks made up of multityped, linked objects, social media networks & bibliographic networks, for example. In this study, we present a path-based similarity join (PS-join) approach to yield the top k comparable pairings of items in a heterogeneous information network depending on any join path given by the user.

Keywords

Bucket Pruning Based Locality Sensitive Hashing, Heterogeneous Network, Similarity Search

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