  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.
