Improving the Compactness in Social Network Thematic Groups by Exploiting a Multi-dimensional User-to-Group Matching Algorithm

Meo, Pasquale De; Messina, Fabrizio; Rosaci, Domenico; Sarné, Giuseppe M. L.

doi:10.1109/incos.2014.71

Cited by 4 publications

(1 citation statement)

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“…Users can form or join existing groups on the basis of shared interests or because dense social connections exist among group members (Meo, Messina, Rosaci, & Sarné, 2014). On the other hand, community-based supervised learning process to detect the set of attributes in a user profile for which it is expected to see a correlation among their attributed values (e.g., job and salary) (Bahri, Carminati, & Ferrari, 2014).…”

Section: Related Workmentioning

confidence: 99%

A New Model for Rating Users’ Profiles in Online Social Networks

Malli¹,

Said²,

Fadlallah³

2017

CIS

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Profiling users in Online Social Networks (OSNs) is of great benefit in multiple domains (e.g., marketing, sociology, and forensics). In this paper, we propose a new model for rating user's profile (i.e., low, medium, high, and advanced) in an OSN community by embedding it into clusters located at predefined range of radius in a low-dimensional Cartesian space. The orthogonal coordinates of the profile are estimated using Principle Component Analysis (PCA) applied on a vector of metrics formulated as a set of attributes of interest (i.e., qualitative and quantitative) mined from the user's profile to characterize his/her level of participation and behavior in the community. The experimentations are conducted on 3000 simulated profiles of three OSNs (Facebook, Twitter and Instagram) by embedding them in three Cartesian spaces of three corresponding communities (Religion, Political and Lifestyle). The results show that we are able to estimate accurately the profile rates by reducing the vector of metrics to a low-dimensional space whittle down to 3-D space.

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Section: Related Workmentioning

confidence: 99%