Self-supervised Dynamic Graph Representation Learning Approach Based on Contrastive Prediction

In recent years,graph self-supervised learning represented by graph contrastive learning has become a hot research to-pic in the field of graph learning.This learning paradigm does not depend on node labels and has good generalization ability.However,most of the existing graph self-supervised learning methods use static graph structures to design learning tasks,such as learning node-level or graph-level representations based on structural contrast,without considering the dynamic information of graph over time.To address this problem,the paper proposes a self-supervised dynamic graph representation learning method based on contrastive prediction(DGCP),which utilizes a contrastive loss inducing the embedding space to capture the most useful information for predicting future graph structures.Firstly,each temporal snapshot graph is encoded using a graph neural network to obtain its corresponding node representation matrix.Then,an autoregressive model is used to predict node representations in the next temporal snapshot graph.Finally,the model is trained end-to-end by using the contrastive loss and sliding window me-chanism.Experimental results on real graph datasets show that DGCP outperforms baseline methods on the link prediction task.