ELM-NeuralWalk: trust evaluation for online social networks

Abstract

Trust relationship plays an important role in online shopping, recommendation systems, internet of things, etc. The problem of trust evaluation among users in online social network (OSN) has attracted much attention, and has become a hot issue in the domain of social computing. However, the way of trust propagation and aggregation in OSN is still not clear, as well as the accuracy of trust calculation. In order to calculate the indirect trust, an ELM-NeuralWalk algorithm to implement trust propagation and aggregation is proposed. ELM-WalkNet firstly learns two-hop trust calculation rules, then calculates two-hop trust among users in the OSN. After that, ELM-NeuralWalk updates the OSN with the calculated trust value, so as to realise the calculation of multi-hop trust among users through iterative calling ELM-WalkNet. Unlike traditional solutions that use inference methods, ELM-WalkNet can learn trust calculation rules in an inductive way and accurately calculate indirect trust between users. Experiments on two real OSN datasets showed that ELM-NeuralWalk outperforms existing solutions.