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。

基于可信度的图卷积网络用于带符号网络嵌入

Trustworthiness-Driven Graph Convolutional Networks for Signed Network  Embedding

Graph convolutional networks (GCNs) is a class of artificial neural networks
for processing data that can be represented as graphs. Since financial
transactions can naturally be constructed as graphs, GCNs are widely applied in
the financial industry, especially for financial fraud detection. In this
paper, we focus on fraud detection on cryptocurrency truct networks. In the
literature, most works focus on static networks. Whereas in this study, we
consider the evolving nature of cryptocurrency networks, and use local
structural as well as the balance theory to guide the training process. More
specifically, we compute motif matrices to capture the local topological
information, then use them in the GCN aggregation process. The generated
embedding at each snapshot is a weighted average of embeddings within a time
window, where the weights are learnable parameters. Since the trust networks is
signed on each edge, balance theory is used to guide the training process.
Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the
proposed model outperforms those in the literature.

本文关注加密货币网络上的欺诈检测，使用图形卷积网络处理这些图形。采用局部结构和平衡理论指导训练，使用基本矩阵捕捉局部拓扑信息，并在 GCN 聚合过程中使用它们，实现在时间窗口内的嵌入加权平均。实验结果表明，本文所提出模型优于文献中的模型。

面向带符号加密货币信任网络的基于时序 GCN 的模式感知欺诈检测

Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks

Due to the fact much of today's data can be represented as graphs, there has
been a demand for generalizing neural network models for graph data. One recent
direction that has shown fruitful results, and therefore growing interest, is
the usage of graph convolutional neural networks (GCNs). They have been shown
to provide a significant improvement on a wide range of tasks in network
analysis, one of which being node representation learning. The task of learning
low-dimensional node representations has shown to increase performance on a
plethora of other tasks from link prediction and node classification, to
community detection and visualization. Simultaneously, signed networks (or
graphs having both positive and negative links) have become ubiquitous with the
growing popularity of social media. However, since previous GCN models have
primarily focused on unsigned networks (or graphs consisting of only positive
links), it is unclear how they could be applied to signed networks due to the
challenges presented by negative links. The primary challenges are based on
negative links having not only a different semantic meaning as compared to
positive links, but their principles are inherently different and they form
complex relations with positive links. Therefore we propose a dedicated and
principled effort that utilizes balance theory to correctly aggregate and
propagate the information across layers of a signed GCN model. We perform
empirical experiments comparing our proposed signed GCN against
state-of-the-art baselines for learning node representations in signed
networks. More specifically, our experiments are performed on four real-world
datasets for the classical link sign prediction problem that is commonly used
as the benchmark for signed network embeddings algorithms.

本文提出了一种基于平衡理论的有向图卷积神经网络模型，用于在带有正负连接的有向图中进行节点表示学习，并证明其在 link sign prediction 等问题上的有效性。