Piecewise maximal similarity for ad-hoc social networks
Published in Wireless Personal Communications, 2017
Recommended citation: Sapna Gambhir, Nagender Aneja, Liyanage De "Piecewise maximal similarity for ad-hoc social networks." Wireless Personal Communications, 2017. vol. 97 pp. 3519--3529 doi: 10.1007/s11277-017-4683-4 https://link.springer.com/article/10.1007/s11277-017-4683-4
Piecewise-maximal-similarity-for-ad-hoc-social-networks
Piecewise-maximal-similarity-for-ad-hoc-social-networks
(Journal Publication)
Abstract: Computing Profile Similarity is a fundamental requirement in Social Networks to suggest similar social connections with a high chance of being accepted as actual connections. Representing and measuring similarity appropriately is a pursuit of many researchers. Cosine similarity is a widely used metric that is simple and effective. This paper analyzes cosine similarity for social profiles and proposes a novel method to compute Piecewise Maximal Similarity between profiles. The proposed metric is 6% more effective in measuring similarity than cosine similarity based on computations on real data.
Recommended citation: ‘Sapna Gambhir, Nagender Aneja, Liyanage De "Piecewise maximal similarity for ad-hoc social networks." Wireless Personal Communications, 2017. vol. 97 pp. 3519–3529 doi: 10.1007/s11277-017-4683-4’