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

A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign Prediction