Name
Abstract | ||
|---|---|---|
Assessing the trust between users in a trust social network (TSN) isa critical issue in many applications, e.g., film recommendation,spam detection, and online lending. Despite of various trust assessment methods, a challenge remaining to existing solutions is how to accurately determine the factors that affect trust propagation and trust fusion within a TSN. To address this challenge, we propose the NeuralWalk algorithm to cope with trust factor estimation and trust relation prediction problems simultaneously. NeuralWalk employs a neural network, named WalkNet, to model single-hop trust propagation and fusion in a TSN. By treating original trust relations in a TSN as labeled samples, WalkNet is able to learn the parameters that will be used for trust computation/assessment. Unlike traditional solutions, WalkNet is able to accurately predict unknown trust relations in an inductive manner. Based on WalkNet, NeuralWalk iteratively assesses the unknown multi-hop trust relations among users via the obtained single-hop trust computation rules. Experiments on two real-world TSN datasets indicate that NeuralWalk significantly outperforms the state-of-the-art solutions. |
Year | Venue | Keywords |
|---|---|---|
2019 | ieee international conference computer and communications | Social networking (online),Computational modeling,Training,Adaptation models,Inference algorithms,Biological neural networks |
Field | DocType | ISSN |
Social network,Computer science,Artificial intelligence,Artificial neural network,Trust factor,Computation,Distributed computing | Conference | 0743-166X |
ISBN | Citations | PageRank |
978-1-7281-0515-4 | 3 | 0.38 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guangchi Liu | 1 | 27 | 3.29 |
| Chenyu Li | 2 | 3 | 0.38 |
| Qing Yang | 3 | 284 | 30.11 |