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
this https URL

通过使用边缘符号的可信性修正图卷积网络（GCN）中的错误嵌入传播，提出了一种名为 TrustSGCN 的新型基于 GCN 的签名网络嵌入方法，该方法利用了平衡理论推断出的高阶关系的边缘符号的可信性。实验证明 TrustSGCN 在四个真实世界的签名网络数据集上始终优于其他五种基于 GCN 的签名网络嵌入方法。