Trust Based Link Prediction Using Fuzzy Computational Model in Social Networks
Subject Areas : AI and Robotics
Fateme Hoseinkhani
1
,
Ali Harounabadi
2
*
,
ُُُُSaeed Setayeshi
3
1 - Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Nuclear Engineering and Physics Dept., Amirkabir University of Technology, Tehran, Iran
Keywords: Link prediction, Signed social networks, Trust, Distrust, Fuzzy computational,
Abstract :
Link prediction is an important to check link between nodes in social networks. The modeling of social networks leads to emergence of signed, directed and weighted social networks. The relationships of users in social networks are characterized by subjective, asymmetric and ambiguous aspects related to this domain, then both terms of trust and distrust are challenging. To solve the problem of sparsity in networks and overcome ambiguity in relationships, a trust-distrust method based on fuzzy computational is proposed to calculate strength of links. The purpose of proposed link prediction is to solve problem of sparsity in signed social networks by combining descriptive features of users with the direct influence of top nodes and the indirect influence of common nodes on rating prediction. Trust is determined by a Mamdani fuzzy system based on mirroring of similarity fuzzy features, overall trust and overall distrust. The evaluation of the proposed method was done with the accuracy measure on datasets of Epinions and Slashdot. The accuracy of proposed method in Epinions and Slashdot datasets is 0.991 and 0.998, respectively. The obtained results show that proposed method works well for problem of data sparsity in signed social networks and show the effectiveness of proposed model.
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