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Deep Graph Neural Point Process for Learning Temporal Interactive Networks
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DOI：10.1007/978-981-95-3349-7_32.png)
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Abstract
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Learning temporal interaction networks(TIN) is previously regarded as a coarse-grained multi-sequence prediction problem, ignoring the network topology structure influence. This paper addresses this limitation and a Deep Graph Neural Point Process(DGNPP) model for TIN is proposed. DGNPP consists of two key modules: the Node Aggregation Layer and the Self Attentive Layer. The Node Aggregation Layer captures topological structures to generate static representation for users and items, while the Self Attentive Layer dynamically updates embeddings over time. By incorporating both dynamic and static embeddings into the event intensity function and optimizing the model via maximum likelihood estimation, DGNPP predicts events and occurrence time effectively. Experimental evaluations on three public datasets demonstrate that DGNPP achieves superior performance in event prediction and time prediction tasks with high efficiency, significantly outperforming baseline models and effectively mitigating the limitations of prior approaches.
Keywords:
Temporal Point Process
Graph Neural Networks
Temporal Interactive Networks
Intensity Function
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Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks.png)
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningIF0
OAAIShareSaveIBPL: Information Bottleneck-based Prompt Learning for graph out-of-distribution detectionNEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
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