Forward Backward Similarity Search in Knowledge Networks

TL;DR

This paper introduces Forward Backward Similarity (FBS), a dual perspective metric for similarity search in networks that considers both query and candidate nodes, improving alignment with human intuition and performance.

Contribution

The paper proposes a novel dual perspective similarity measure, FBS, which enhances network similarity search by incorporating both query and target node perspectives.

Findings

FBS outperforms existing algorithms in community overlap and link prediction.

FBS aligns better with human judgment in similarity ranking.

FBS captures semantic relationships more intuitively.

Abstract

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.