INTERTWINED-GNN: A GRAPH NEURAL NETWORK FOR LEARNING EMBEDDINGS ON HETEROGENEOUS GRAPHS

    公开(公告)号:US20250111189A1

    公开(公告)日:2025-04-03

    申请号:US18375073

    申请日:2023-09-29

    Abstract: In an embodiment, a computer hosts and operates an input neural layer of an artificial neural network that generates, based on all of the features of a first vertex of a first vertex type in a graph, an embedding of the first vertex. The embedding of the first vertex has a predefined size that does not depend on the first vertex type. The input neural layer generates, based on all of the features of a first edge of a first edge type in the graph, an embedding of the first edge. A subsequent neural layer of the artificial neural network generates an embedding of a second vertex of a second vertex type in the graph, and this generating is based on: the embedding of the first vertex and all of the features of the second vertex, including a particular feature that is not a feature of the first vertex type.

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