The Ties that matter: From the perspective of Similarity Measure in Online Social Networks
This work addresses the need for better connection strength measures in social network analysis, but it is incremental as it builds on existing similarity-based techniques.
The paper tackles the problem of measuring connection strength in online social networks by proposing an asymmetric edge similarity measure called NDES, which incorporates neighborhood density and directionality, and shows it improves community detection accuracy and quality compared to three existing measures on small real-world datasets.
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.