Chen Jiawang, Qiao Zhi, Yan Jun, Wu Zhenqiang
School of Computer Science, Shaanxi Normal University, Xi'an 710062, China.
Entropy (Basel). 2022 Dec 25;25(1):38. doi: 10.3390/e25010038.
Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and thus limit the ability to learn the underlying structure of real signed graphs. To address this limitation, a deep graph neural network framework SiNP to learn Signed network embedding with Node Polarity is proposed. To be more explicit, a node-signed property metric mechanism is developed to encode the individual characteristics of the nodes. In addition, a graph convolution layer is added so that both positive and negative information from neighboring nodes can be combined. The final embedding of nodes is produced by concatenating the outcomes of these two portions. Finally, extensive experiments have been conducted on four significant real-world signed network datasets to demonstrate the efficiency and superiority of the proposed method in comparison to the state-of-the-art.
带符号图神经网络为具有正链接和负链接的带符号网络中的节点学习低维表示，这有助于诸如链接预测等许多下游任务。然而，大多数现有的带符号图神经网络忽略了节点的个体特征，从而限制了学习真实带符号图潜在结构的能力。为了解决这一限制，提出了一种深度图神经网络框架SiNP，用于学习具有节点极性的带符号网络嵌入。更具体地说，开发了一种节点带符号属性度量机制来编码节点的个体特征。此外，添加了一个图卷积层，以便可以组合来自相邻节点的正信息和负信息。节点的最终嵌入是通过连接这两部分的结果产生的。最后，在四个重要的真实世界带符号网络数据集上进行了广泛的实验，以证明所提出的方法与现有技术相比的效率和优越性。
