Deep Graph Neural Point Process For Learning Temporal Interactive Networks

• URL: http://arxiv.org/abs/2508.13219v1
• Date: Sun, 17 Aug 2025 11:17:03 GMT
• Title: Deep Graph Neural Point Process For Learning Temporal Interactive Networks
• Authors: Su Chen, Xiaohua Qi, Xixun Lin, Yanmin Shang, Xiaolin Xu, Yangxi Li,
• Abstract summary: Learning temporal interaction networks(TIN) is previously regarded as a coarse-grained multi-sequence prediction problem.<n>This paper addresses this limitation and proposes a Deep Graph Neural Point Process(DGNPP) model for TIN.<n> Experimental evaluations on three public datasets demonstrate that DGNPP achieves superior performance in event prediction and time prediction tasks.
• Score: 8.207618516075183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring [6.125909573003906]
  We propose a unified, interpretable GNN framework that advances the state of the art along three key axes.<n>First, we compare prefix-based Graph Convolutional Networks(GCNs) and full trace Graph Attention Networks(GATs) to quantify the performance gap between localized and global modeling.<n>Second, we introduce a novel time decay attention mechanism that constructs dynamic, prediction-centered windows.<n>Third, we embed transition type semantics into edge features to enable fine grained reasoning over structurally ambiguous traces.
  arXiv  Detail & Related papers  (2025-08-13T06:21:42Z)
• Dynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks [14.77536193242342]
  Spiking Dynamic Graph Network is a novel framework that leverages the temporal processing capabilities of spiking neural networks (SNNs) and spike-dependent plasticity (STD-P)<n>It adapts to any dataset by learning dynamic-temporal dependencies directly from event data, enhancing generalizability and modeling.<n>Our evaluations conducted on both synthetic and real-world datasets including NYC Taxi, 911 Reddit, and Stack Overflow, demonstrate superior accuracy while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-04-01T23:23:10Z)
• TempGNN: Temporal Graph Neural Networks for Dynamic Session-Based Recommendations [5.602191038593571]
  Temporal Graph Neural Networks (TempGNN) is a generic framework for capturing the structural and temporal dynamics in complex item transitions. TempGNN achieves state-of-the-art performance on two real-world e-commerce datasets.
  arXiv  Detail & Related papers  (2023-10-20T03:13:10Z)
• Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting [60.03169701753824]
  We propose a novel Dynamic Diffusion-al Graph Neural Network (DVGNN) fortemporal forecasting. The proposed DVGNN model outperforms state-of-the-art approaches and achieves outstanding Root Mean Squared Error result.
  arXiv  Detail & Related papers  (2023-05-16T11:38:19Z)
• Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation [67.26422477327179]
  Temporal graphs exhibit dynamic interactions between nodes over continuous time. We propose a novel method of temporal graph convolution with the whole neighborhood. Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
  arXiv  Detail & Related papers  (2023-04-15T08:17:18Z)
• EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning [92.71579608528907]
  This paper aims to design an easy-to-use pipeline (termed as EasyDGL) composed of three key modules with both strong ability fitting and interpretability. EasyDGL can effectively quantify the predictive power of frequency content that a model learn from the evolving graph data.
  arXiv  Detail & Related papers  (2023-03-22T06:35:08Z)
• Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction [52.0977259978343]
  This paper proposes a Continuous-time and Multi-level dynamic graph representation learning method for Origin-Destination demand prediction (CMOD) The state vectors keep historical transaction information and are continuously updated according to the most recently happened transactions. Experiments are conducted on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-30T03:37:50Z)
• CEP3: Community Event Prediction with Neural Point Process on Graph [59.434777403325604]
  We propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP) Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.
  arXiv  Detail & Related papers  (2022-05-21T15:30:25Z)
• Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations [3.286961611175469]
  We propose a new deep learning-based sequential recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation. It realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions.
  arXiv  Detail & Related papers  (2021-07-12T07:34:20Z)
• Policy-GNN: Aggregation Optimization for Graph Neural Networks [60.50932472042379]
  Graph neural networks (GNNs) aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. We propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process.
  arXiv  Detail & Related papers  (2020-06-26T17:03:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.