  Online Social Networks have embarked on the importance of connection strength
measures which has a broad array of applications such as, analyzing diffusion
behaviors, community detection, link predictions, recommender systems. Though
there are some existing connection strength measures, the density that a
connection shares with it's neighbors and the directionality aspect has not
received much attention. In this paper, we have proposed an asymmetric edge
similarity measure namely, Neighborhood Density-based Edge Similarity (NDES)
which provides a fundamental support to derive the strength of connection. The
time complexity of NDES is $O(nk^2)$. An application of NDES for community
detection in social network is shown. We have considered a similarity based
community detection technique and substituted its similarity measure with NDES.
The performance of NDES is evaluated on several small real-world datasets in
terms of the effectiveness in detecting communities and compared with three
widely used similarity measures. Empirical results show NDES enables detecting
comparatively better communities both in terms of accuracy and quality.
