The present disclosure proposes techniques (e.g., a method and a system) for generating temporal heterophilic graph neural network (THGNN) for event-based graphs. All existing (temporal) GNNs typically follow temporal homophily assumption, while the real-world graphs are largely heterophilic. To solve this problem, the proposed disclosure first defines the graph heterophily based on edge heterophily and then develops a novel method/technique to automatically detect the homophily/heterophily weight of graph nodes that should be regarded as the focal node’s homophilic/heterophilic neighbours. Finally, a neighbourhood sampling based GNN is adopted. In this manner, the proposed disclosure defines heterophily for temporal graphs and proposes a novel adaptive method to select right neighbourhood for graph aggregation.

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.