Exploring Time Granularity on Temporal Graphs for Dynamic Link Prediction in Real-world Networks

• URL: http://arxiv.org/abs/2311.12255v2
• Date: Wed, 22 Nov 2023 19:44:10 GMT
• Title: Exploring Time Granularity on Temporal Graphs for Dynamic Link Prediction in Real-world Networks
• Authors: Xiangjian Jiang, Yanyi Pu
• Abstract summary: Dynamic Graph Neural Networks (DGNNs) have emerged as the predominant approach for processing dynamic graph-structured data. In this paper, we explore the impact of time granularity when training DGNNs on dynamic graphs through extensive experiments.
• Score: 0.48346848229502226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic Graph Neural Networks (DGNNs) have emerged as the predominant approach for processing dynamic graph-structured data. However, the influence of temporal information on model performance and robustness remains insufficiently explored, particularly regarding how models address prediction tasks with different time granularities. In this paper, we explore the impact of time granularity when training DGNNs on dynamic graphs through extensive experiments. We examine graphs derived from various domains and compare three different DGNNs to the baseline model across four varied time granularities. We mainly consider the interplay between time granularities, model architectures, and negative sampling strategies to obtain general conclusions. Our results reveal that a sophisticated memory mechanism and proper time granularity are crucial for a DGNN to deliver competitive and robust performance in the dynamic link prediction task. We also discuss drawbacks in considered models and datasets and propose promising directions for future research on the time granularity of temporal graphs.

Related papers

• DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs [59.434893231950205]
  Dynamic graph learning aims to uncover evolutionary laws in real-world systems. We propose DyG-Mamba, a new continuous state space model for dynamic graph learning. We show that DyG-Mamba achieves state-of-the-art performance on most datasets.
  arXiv  Detail & Related papers  (2024-08-13T15:21:46Z)
• DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning [38.53424185696828]
  The representation learning of Discrete-Time Dynamic Graphs (DTDGs) has been extensively applied to model the dynamics of temporally changing entities and their evolving connections. This paper introduces a novel representation learning method DTFormer for DTDGs, pivoting from the traditional GNN+RNN framework to a Transformer-based architecture.
  arXiv  Detail & Related papers  (2024-07-26T05:46:23Z)
• Gradient Transformation: Towards Efficient and Model-Agnostic Unlearning for Dynamic Graph Neural Networks [66.70786325911124]
  Graph unlearning has emerged as an essential tool for safeguarding user privacy and mitigating the negative impacts of undesirable data. With the increasing prevalence of DGNNs, it becomes imperative to investigate the implementation of dynamic graph unlearning. We propose an effective, efficient, model-agnostic, and post-processing method to implement DGNN unlearning.
  arXiv  Detail & Related papers  (2024-05-23T10:26:18Z)
• A survey of dynamic graph neural networks [26.162035361191805]
  Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and learning from graph-structured data. This paper provides a comprehensive review of the fundamental concepts, key techniques, and state-of-the-art dynamic GNN models.
  arXiv  Detail & Related papers  (2024-04-28T15:07:48Z)
• FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective [48.00240550685946]
  Current state-of-the-art graph neural network (GNN)-based forecasting methods usually require both graph networks (e.g., GCN) and temporal networks (e.g., LSTM) to capture inter-series (spatial) dynamics and intra-series (temporal) dependencies, respectively. We propose a novel Fourier Graph Neural Network (FourierGNN) by stacking our proposed Fourier Graph Operator (FGO) to perform matrix multiplications in Fourier space. Our experiments on seven datasets have demonstrated superior performance with higher efficiency and fewer parameters compared with state-of-the-
  arXiv  Detail & Related papers  (2023-11-10T17:13:26Z)
• DyExplainer: Explainable Dynamic Graph Neural Networks [37.16783248212211]
  We present DyExplainer, a novel approach to explaining dynamic Graph Neural Networks (GNNs) on the fly. DyExplainer trains a dynamic GNN backbone to extract representations of the graph at each snapshot. We also augment our approach with contrastive learning techniques to provide priori-guided regularization.
  arXiv  Detail & Related papers  (2023-10-25T05:26:33Z)
• 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)
• Analysis of different temporal graph neural network configurations on dynamic graphs [0.0]
  This project aims to address the gap in the literature by performing a qualitative analysis of spatial-temporal dependence structure learning on dynamic graphs. An extensive ablation study will be conducted on different variants of the best-performing TGN to identify the key factors contributing to its performance. By achieving these objectives, this project will provide valuable insights into the design and optimization of TGNs for dynamic graph analysis.
  arXiv  Detail & Related papers  (2023-05-02T00:07:33Z)
• 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)
• Temporal Domain Generalization with Drift-Aware Dynamic Neural Network [12.483886657900525]
  We propose a Temporal Domain Generalization with Drift-Aware Dynamic Neural Network (DRAIN) framework. Specifically, we formulate the problem into a Bayesian framework that jointly models the relation between data and model dynamics. It captures the temporal drift of model parameters and data distributions and can predict models in the future without the presence of future data.
  arXiv  Detail & Related papers  (2022-05-21T20:01:31Z)
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
  We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
  arXiv  Detail & Related papers  (2022-02-17T02:17:31Z)

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.