Unsupervised heterophilic edge graph analysis model and analysis method using same

Abstract

The present application relates to an unsupervised heterophilic edge graph analysis model using an edge discriminator and a multi-channel encoder, and a learning method using the same, and generates a feature information-based representation, a connection information-based representation and a weighted graph-based representation. Then, these representations are combined to generate a final node representation vector, and the consistency of the representation is enhanced through contrastive learning. Compared with the conventional single channel model, the node representation power is increased, which leads to excellent node classification accuracy. In addition, the model of the present application may be applied to unlabeled datasets through unsupervised learning, and particularly exhibits excellent performance on heterophilic edge graph. The present application may be applied to data analysis with complex relationships in the fields such as social network analysis, recommendation systems, and bioinformatics, and may be utilized for various graph-based tasks such as anomaly detection and link prediction.

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