Multi-view hyperbolic-hyperbolic graph representation learning method

Abstract

The present disclosure belongs to application in the field of deep learning and graph neural networks, and particularly relates to a multi-view hyperbolic-hyperbolic graph representation learning method. Two views are constructed based on a topological relation of nodes and node attributes, then an adjacency matrix and the two views generated are input into a hyperbolic-hyperbolic graph neural network to obtain node representations of three views, graph embedding representations of different views are obtained by performing hyperbolic-hyperbolic convolution and pooling on the node representations of the three views, the graph embedding representations are concatenated and input into a Lorentz multilayer perceptron (MLP) layer to obtain attention scores of the views, and with a hyperbolic-hyperbolic weighted representation, a multi-view based node embedding representation is obtained.

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