Abstract
Evaluating trust and distrust between users in online social networks is an important research problem. To address this problem, we provide a method for estimating continuous trust /distrust value between unconnected users. Our method is based on co-citation and transpose trust propagation. We determine on average how differently two users trust or are trusted by other users, and how differently a user trusts another user from how it is trusted by that user. Using these differences, we estimate four partial trust estimates and compute the final trust value from trustor to trustee as the weighted average of these partial estimates. We propose a basic framework that maximizes accuracy, robustness and coverage and show how we can further improve the accuracy at a lower coverage. We perform experiments on real world trust related networks that show that our proposed method outperforms recent state of the art trust computation methods in terms of accuracy and robustness on commonly used datasets.
Highlights
In online social networks (OSNs) trust plays an important role in user activities
Knowing the level of trust or distrust between users is valuable for OSN service providers, as it can be used for tasks such as suggesting friends, detecting malicious or spam users and community detection
We use co-citation and transpose trust propagation and develop a method for estimating continuous trust/distrust value that is more accurate and robust other recent existing algorithms and is efficient to be applied on large networks
Summary
In online social networks (OSNs) trust plays an important role in user activities. It allows users to distinguish between reliable and malicious users and content produced by them. By using chain of trust links or paths in the trust graph, the level of trust is computed These algorithms need to extract all, or a very large number of paths and are not efficient on large networks. With availability of more datasets with both trust and distrust information, a few algorithms [15]–[17] have been developed that estimate both trust and distrust as a continuous value Some of these algorithms are not efficient to be applied on very large networks or are not based on trust propagation. Their accuracy and robustness can be further improved For this purpose, we use co-citation and transpose trust propagation and develop a method for estimating continuous trust/distrust value that is more accurate and robust other recent existing algorithms and is efficient to be applied on large networks. We use information from four sources: 1. Trustee’s in-neighbors: We find how differently trustee’s in-neighbors and the trustor trust some of the other users and use this difference along with trustee’s in-neighbors trust of trustee to find a partial trust estimate based on trustee’s in-neighbors
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