Forward Backward Similarity Search in Knowledge Networks
This work addresses the issue of unintuitive similarity results in social and knowledge networks like GitHub and Wikipedia, offering an incremental improvement by considering both query and target node perspectives.
The paper tackled the problem of similarity search in knowledge networks by proposing a dual perspective metric called Forward Backward Similarity (FBS), which outperformed other algorithms on community overlap and link prediction and matched human preferences in evaluations on four large real-world 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 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.