Authors:
Sana Hamdi
1
;
Alda Lopes Gançarski
2
;
Amel Bouzeghoub
3
and
Sadok Ben Yahia
1
Affiliations:
1
University of Tunis El-Manar, SAMOVAR and Telecom SudParis, Tunisia
;
2
University of Tunis El-Manar, Tunisia
;
3
SAMOVAR and Telecom SudParis, France
Keyword(s):
Social Networks, Reputation, Trust, Clustering.
Related
Ontology
Subjects/Areas/Topics:
Data and Application Security and Privacy
;
Information and Systems Security
;
Trust Management and Reputation Systems
Abstract:
Trust and reputation management stands as a corner stone within the Online Social Networks (OSNs) since
they ensure a healthy collaboration relationship among participants. Currently, most trust and reputation systems
focus on evaluating the credibility of the users. The reputation systems in OSNs have as objective to help
users to make difference between trustworthy and untrustworthy, and encourage honest users by rewarding
them with high trust values. Computing reputation of one user within a network requires knowledge of trust
degrees between the users. In this paper, we propose a new Clustering Reputation algorithm, called RepC,
based on trusted network. This algorithm classifies the users of OSNs by their trust similarity such that most
trustworthy users belong to the same cluster. We conduct extensive experiments on a real online social network
dataset from Twitter. Experimental results show that our algorithm generates better results than do the
pioneering approaches of the li
terature.
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