Relation Representation Learning via Signed Graph Mutual Information Maximization for Trust Prediction

Yongjun Jing,Xing Huo,Kun Shao,Hao Wang

doi:10.3390/sym13010115

Abstract

Trust prediction is essential to enhancing reliability and reducing risk from the unreliable node, especially for online applications in open network environments. An essential fact in trust prediction is to measure the relation of both the interacting entities accurately. However, most of the existing methods infer the trust relation between interacting entities usually rely on modeling the similarity between nodes on a graph and ignore semantic relation and the influence of negative links (e.g., distrust relation). In this paper, we proposed a relation representation learning via signed graph mutual information maximization (called SGMIM). In SGMIM, we incorporate a translation model and positive point-wise mutual information to enhance the relation representations and adopt Mutual Information Maximization to align the entity and relation semantic spaces. Moreover, we further develop a sign prediction model for making accurate trust predictions. We conduct link sign prediction in trust networks based on learned the relation representation. Extensive experimental results in four real-world datasets on trust prediction task show that SGMIM significantly outperforms state-of-the-art baseline methods.

Highlights

Graph representation learning (GRL), as one of the most popular and promising machine learning techniques on graphs, has been successfully applied to diverse graph analysis tasks, such as link prediction [1,2,3], node classification [4,5,6], molecular generation [7,8,9], community detection [10,11], and demonstrated to be quite useful
In order to further overcome the problem of insufficient capture of latent features and semantic information of negative edges while learning the relation representation model in a unified semantic space, we propose a signed graph representation learning framework via signed graph mutual information maximization (SGMIM)
Inspired by recent self-learning work with MI maximization [57,58] and knowledge graph embedding (KGE) [59,60,61], we propose a relation representation learning framework via signed graph mutual information maximization, and use learned vector representation as the input of neural networks to perform the task of trust prediction

Summary

Introduction

Graph representation learning (GRL), as one of the most popular and promising machine learning techniques on graphs, has been successfully applied to diverse graph analysis tasks, such as link prediction [1,2,3], node classification [4,5,6], molecular generation [7,8,9], community detection [10,11], and demonstrated to be quite useful. GRL aims to learn a low-dimensional latent vector representation of nodes or edges while making the vector preserve as much more structure and feature information as possible. Most of the GRL approaches focus more on the vector representation of nodes, in which abundant edge information has not been well captured and used, especially in social networks with complex relationships. With the growth of online social networks, signed networks are becoming increasingly ubiquitous. A singed network is a classic directed graph with two kinds of edges (positive or negative), where the positive edges are usually seen as similarity or collaboration, while negative edges represent opposites and differences [12]. In the Epinions network, positive edges mean trust, while negative edges represent distrust

Methods

Results

Conclusion