Temporal Graph ODEs for Irregularly-Sampled Time Series

• URL: http://arxiv.org/abs/2404.19508v1
• Date: Tue, 30 Apr 2024 12:43:11 GMT
• Title: Temporal Graph ODEs for Irregularly-Sampled Time Series
• Authors: Alessio Gravina, Daniele Zambon, Davide Bacciu, Cesare Alippi,
• Abstract summary: We introduce the Temporal Graph Ordinary Differential Equation (TG-ODE) framework, which learns both the temporal and spatial dynamics from graph streams where the intervals between observations are not regularly spaced. We empirically validate the proposed approach on several graph benchmarks, showing that TG-ODE can achieve state-of-the-art performance in irregular graph stream tasks.
• Score: 32.68671699403658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e.g., social networks and physical systems are characterized by continuous dynamics and sporadic observations. To address this limitation, we introduce the Temporal Graph Ordinary Differential Equation (TG-ODE) framework, which learns both the temporal and spatial dynamics from graph streams where the intervals between observations are not regularly spaced. We empirically validate the proposed approach on several graph benchmarks, showing that TG-ODE can achieve state-of-the-art performance in irregular graph stream tasks.

Related papers

• State Space Models on Temporal Graphs: A First-Principles Study [30.531930200222423]
  Research on deep graph learning has shifted from static graphs to temporal graphs in response to real-world complex systems that exhibit dynamic behaviors. Sequence models such as RNNs or Transformers have long been the predominant backbone networks for modeling such temporal graphs. We develop GraphSSM, a graph state space model for modeling the dynamics of temporal graphs.
  arXiv  Detail & Related papers  (2024-06-03T02:56:11Z)
• Graph-Level Embedding for Time-Evolving Graphs [24.194795771873046]
  Graph representation learning (also known as network embedding) has been extensively researched with varying levels of granularity. We present a novel method for temporal graph-level embedding that addresses this gap.
  arXiv  Detail & Related papers  (2023-06-01T01:50:37Z)
• 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 Graph Neural Networks for Irregular Data [14.653008985229615]
  TGNN4I model is designed to handle both irregular time steps and partial observations of the graph. Time-continuous dynamics enables the model to make predictions at arbitrary time steps. Experiments on simulated data and real-world data from traffic and climate modeling validate the usefulness of both the graph structure and time-continuous dynamics.
  arXiv  Detail & Related papers  (2023-02-16T16:47:55Z)
• DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph [59.583555454424]
  We propose a novel disenTangled representation learning framework for discrete-time Dynamic graphs, namely DyTed. We specially design a temporal-clips contrastive learning task together with a structure contrastive learning to effectively identify the time-invariant and time-varying representations respectively.
  arXiv  Detail & Related papers  (2022-10-19T14:34:12Z)
• Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution [60.695162101159134]
  Existing works merely view a dynamic graph as a sequence of changes. We formulate dynamic graphs as temporal edge sequences associated with joining time of. vertex and timespan of edges. A time-aware Transformer is proposed to embed. vertex' dynamic connections and ToEs into the learned. vertex representations.
  arXiv  Detail & Related papers  (2022-07-01T15:32:56Z)
• Continuous Temporal Graph Networks for Event-Based Graph Data [41.786721257905555]
  We propose Continuous Temporal Graph Networks (CTGNs) to capture the continuous dynamics of temporal graph data. Key idea is to use neural ordinary differential equations (ODE) to characterize the continuous dynamics of node representations over dynamic graphs. Experiment results on both transductive and inductive tasks demonstrate the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2022-05-31T16:17:02Z)
• 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)
• Efficient Dynamic Graph Representation Learning at Scale [66.62859857734104]
  We propose Efficient Dynamic Graph lEarning (EDGE), which selectively expresses certain temporal dependency via training loss to improve the parallelism in computations. We show that EDGE can scale to dynamic graphs with millions of nodes and hundreds of millions of temporal events and achieve new state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2021-12-14T22:24:53Z)
• Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting [75.10017445699532]
  Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem. We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
  arXiv  Detail & Related papers  (2021-11-25T08:45:14Z)

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.