ML-ELM-NeuralWalk：在线社交网络的信任评估

ML-ELM-NeuralWalk: Trust Evaluation for Online Social Networks

摘要: 信任关系在网上购物、推荐系统、物联网等方面发挥着重要作用。在线社交网络(OSN)中用户之间的信任评估问题引起了广泛关注，已成为社会计算领域的一个热点问题。但是，在OSN中信任的传递和聚合方式以及信任计算的准确性还不清楚。首先，ML-ELM-WalkNet学习两跳信任计算规则，计算OSN内用户之间的两跳信任。然后，ML-ELM-NeuralWalk用计算出的信任值更新OSN，通过迭代调用ML-ELM- WalkNet实现用户间多跳信任的计算。与采用推断方式的传统解决方案不同，ML-ELM-WalkNet能够以归纳的方式学习信任计算规则，并准确计算出用户之间的间接信任。在两个实际OSN数据集上进行的实验表明，ML-ELM-NeuralWalk的性能优于现有的解决方案。

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 ML-ELM-NeuralWalk algorithm to implement trust propagation and aggregation is proposed. ML-ELM-WalkNet firstly learns two-hop trust calculation rules, calculates two-hop trust among users in the OSN. After that, ML-ELM- NeuralWalk updates the OSN with the calculated trust value, so as to realize the calculation of multi-hop trust among users through iterative calling ML-ELM-WalkNet. Unlike traditional solutions that use inference methods, ML-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 ML-ELM-NeuralWalk outperforms existing solutions.