  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.
