In this paper, we consider the problem of inferring the sign of a link based on limited sign data in signed networks. Regarding this link sign prediction problem, SDGNN (Signed Directed Graph Neural Networks) provides the best prediction performance currently to the best of our knowledge. In this paper, we propose a different link sign prediction architecture call SELO (Subgraph Encoding via Linear Optimization), which obtains overall leading prediction performances compared the state-of-the-art algorithm SDGNN. The proposed model utilizes a subgraph encoding approach to learn edge embeddings for signed directed networks. In particular, a signed subgraph encoding approach is introduced to embed each subgraph into a likelihood matrix instead of the adjacency matrix through a linear optimization method. Comprehensive experiments are conducted on six real-world signed networks with AUC, F1, micro-F1, and Macro-F1 as the evaluation metrics. The experiment results show that the proposed SELO model outperforms existing baseline feature-based methods and embedding-based methods on all the six real-world networks and in all the four evaluation metrics.

翻译：本文研究了在有符号网络中基于有限符号数据推断链接符号的问题。针对这一链接符号预测问题，据我们所知，SDGNN（有符号有向图神经网络）目前提供了最优的预测性能。本文提出了一种不同的链接符号预测架构，称为SELO（基于线性优化的子图编码），与当前最先进的算法SDGNN相比，该架构在整体预测性能上表现领先。所提出的模型利用子图编码方法学习有符号有向网络的边嵌入。特别地，引入了一种有符号子图编码方法，通过线性优化方式将每个子图嵌入到似然矩阵而非邻接矩阵中。我们在六个真实世界的有符号网络上进行了全面实验，使用AUC、F1、微平均F1和宏平均F1作为评估指标。实验结果表明，所提出的SELO模型在所有六个真实世界网络和所有四个评估指标上均优于现有的基于特征的方法和基于嵌入的方法。