Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding

TL;DR

This paper introduces TrustSGCN, a novel graph convolutional network approach for signed network embedding that incorporates trustworthiness of edge signs and societal theories to improve embedding accuracy.

Contribution

TrustSGCN corrects GCN embedding propagation by integrating trustworthiness of edge signs and societal theories, addressing limitations of previous methods relying solely on balance theory.

Findings

TrustSGCN outperforms five state-of-the-art GCN-based SNE methods on four real-world datasets.

Incorporates trustworthiness measurement to improve embedding quality.

Leverages societal theories for more accurate signed network representations.

Abstract

The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCN) have been proposed for this problem, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real-world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect embedding propagation in GCN by utilizing the trustworthiness on edge signs for high-order relationships inferred by the balance theory. The proposed approach consists of three modules: (M1) generation of each node's extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings. Furthermore, TrustSGCN learns the node embeddings by leveraging two well-known societal theories, i.e., balance and status. The experiments on four real-world signed network datasets demonstrate that TrustSGCN consistently outperforms five state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN.