Towards Strength-sensitive Social Profiling in Ego Networks

Asma Chader, Hamid Haddadou, Leila Hamdad, Walid-Khaled Hidouci

Abstract

In online social networks, the incomplete or noisy data are usual conditions raising increasingly the need for more accurate methods; especially in user attribute profiling. This work explores the influence of social tie strength in such settings, based on the intuition that the stronger the relationship is, the more likely its members are to share the same attribute values. A Strength-sensitive community-based social profiling process, named SCoBSP, is introduced under this research and the above hypothesis is tested on real world co-authorship networks from the DBLP computer science bibliography. Experimental results demonstrate the ability of SCoBSP to infer attributes accurately, achieving an improvement of 9.18 % in terms of F-measure over the strength-agnostic process.

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