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Graph contrastive learning has become a powerful technique for several graphmining tasks. It learns discriminative representation from differentperspectives of augmented graphs. Ubiquitous in our daily life, singed-directedgraphs are the most complex and tricky to analyze among various graph types.That is why singed-directed graph contrastive learning has not been studiedmuch yet, while there are many contrastive studies for unsigned and undirected.Thus, this paper proposes a novel signed-directed graph contrastive learning,SDGCL. It makes two different structurally perturbed graph views and gets noderepresentations via magnetic Laplacian perturbation. We use a node-levelcontrastive loss to maximize the mutual information between the two graphviews. The model is jointly learned with contrastive and supervised objectives.The graph encoder of SDGCL does not depend on social theories or predefinedassumptions. Therefore it does not require finding triads or selectingneighbors to aggregate. It leverages only the edge signs and directions viamagnetic Laplacian. To the best of our knowledge, it is the first to introducemagnetic Laplacian perturbation and signed spectral graph contrastive learning.The superiority of the proposed model is demonstrated through exhaustiveexperiments on four real-world datasets. SDGCL shows better performance thanother state-of-the-art on four evaluation metrics.

Signed Directed Graph Contrastive Learning with Laplacian Augmentation