Temporal Graph Pattern Machine

• URL: http://arxiv.org/abs/2601.22454v1
• Date: Fri, 30 Jan 2026 01:46:13 GMT
• Title: Temporal Graph Pattern Machine
• Authors: Yijun Ma, Zehong Wang, Weixiang Sun, Yanfang Ye,
• Abstract summary: Temporal Graph Pattern Machine (TGPM) conceptualizes each interaction as an interaction patch synthesized via temporally-biased random walks.<n>TGPM consistently achieves state-of-the-art performance in both transductive and inductive link prediction.
• Score: 17.352525018007473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal graph learning is pivotal for deciphering dynamic systems, where the core challenge lies in explicitly modeling the underlying evolving patterns that govern network transformation. However, prevailing methods are predominantly task-centric and rely on restrictive assumptions -- such as short-term dependency modeling, static neighborhood semantics, and retrospective time usage. These constraints hinder the discovery of transferable temporal evolution mechanisms. To address this, we propose the Temporal Graph Pattern Machine (TGPM), a foundation framework that shifts the focus toward directly learning generalized evolving patterns. TGPM conceptualizes each interaction as an interaction patch synthesized via temporally-biased random walks, thereby capturing multi-scale structural semantics and long-range dependencies that extend beyond immediate neighborhoods. These patches are processed by a Transformer-based backbone designed to capture global temporal regularities while adapting to context-specific interaction dynamics. To further empower the model, we introduce a suite of self-supervised pre-training tasks -- specifically masked token modeling and next-time prediction -- to explicitly encode the fundamental laws of network evolution. Extensive experiments show that TGPM consistently achieves state-of-the-art performance in both transductive and inductive link prediction, demonstrating exceptional cross-domain transferability.

Related papers

• MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models [51.506429027626005]
  Memory for Time Series (MEMTS) is a lightweight and plug-and-play method for retrieval-free domain adaptation in time series forecasting.<n>Key component of MEMTS is a Knowledge Persistence Module (KPM), which internalizes domain-specific temporal dynamics.<n>This paradigm shift enables MEMTS to achieve accurate domain adaptation with constant-time inference and near-zero latency.
  arXiv  Detail & Related papers  (2026-02-14T14:00:06Z)
• EntroPE: Entropy-Guided Dynamic Patch Encoder for Time Series Forecasting [50.794700596484894]
  We propose EntroPE (Entropy-Guided Dynamic Patch), a novel, temporally informed framework that dynamically detects transition points via conditional entropy.<n>This preserves temporal structure while retaining the computational benefits of patching.<n> Experiments across long-term forecasting benchmarks demonstrate that EntroPE improves both accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-09-30T12:09:56Z)
• Learning Time-Aware Causal Representation for Model Generalization in Evolving Domains [50.66049136093248]
  We develop a time-aware structural causal model (SCM) that incorporates dynamic causal factors and the causal mechanism drifts.<n>We show that our method can yield the optimal causal predictor for each time domain.<n>Results on both synthetic and real-world datasets exhibit that SYNC can achieve superior temporal generalization performance.
  arXiv  Detail & Related papers  (2025-06-21T14:05:37Z)
• DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting [19.064628208136273]
  We propose a Distribution Relation and Adaptive Network (DRAN) capable of dynamically adapting to and distribution changes over time.<n>A Spatial Factor Learner (SFL) module is developed that enables the normalization de-normalization process.<n>Our approach outperforms state-of-the-art methods on weather prediction and traffic flow forecasting tasks.
  arXiv  Detail & Related papers  (2025-04-02T09:18:43Z)
• Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs [3.833708891059351]
  Community-aware Temporal Walks (CTWalks) is a novel framework for representation learning on continuous-time dynamic graphs.<n>CTWalks integrates a community-based parameter-free temporal walk sampling mechanism, an anonymization strategy enriched with community labels, and an encoding process.<n> Experiments on benchmark datasets demonstrate that CTWalks outperforms established methods in temporal link prediction tasks.
  arXiv  Detail & Related papers  (2025-01-21T04:16:46Z)
• SFADNet: Spatio-temporal Fused Graph based on Attention Decoupling Network for Traffic Prediction [4.868638426254428]
  This paper proposes an innovative traffic flow prediction network, SFADNet, which categorizes traffic flow into multiple traffic patterns based on spatial feature matrices.<n>For each pattern, we construct an independent adaptive-temporal fusion graph based on a cross-attention mechanism, employing residual graph convolution modules and time series modules.<n>Extensive experimental results demonstrate that SFADNet outperforms current state-of-the-art baseline across large four-scale datasets.
  arXiv  Detail & Related papers  (2025-01-07T09:09:50Z)
• A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics [51.147876395589925]
  A non-stationary PGDS is proposed to allow the underlying transition matrices to evolve over time. A fully-conjugate and efficient Gibbs sampler is developed to perform posterior simulation. Experiments show that, in comparison with related models, the proposed non-stationary PGDS achieves improved predictive performance.
  arXiv  Detail & Related papers  (2024-02-26T04:39:01Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion [61.751257172868186]
  Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities by exploiting graph and temporal dynamics. Existing DyGNNs fail to handle distribution shifts, which naturally exist in dynamic graphs.
  arXiv  Detail & Related papers  (2023-11-24T02:42:42Z)
• GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks [24.323017830938394]
  This work aims to address challenges by introducing a pre-training framework that seamlessly integrates with baselines and enhances their performance. The framework is built upon two key designs: (i) We propose a. apple-to-apple mask autoencoder as a pre-training model for learning-temporal dependencies. These modules are specifically designed to capture intra-temporal customized representations and semantic- and inter-cluster relationships.
  arXiv  Detail & Related papers  (2023-11-07T02:36:24Z)
• Using Motif Transitions for Temporal Graph Generation [0.0]
  We develop a practical temporal graph generator to generate synthetic temporal networks with realistic global and local features. Our key idea is modeling the arrival of new events as temporal motif transition processes. We demonstrate that our model consistently outperforms the baselines with respect to preserving various global and local temporal graph statistics and runtime performance.
  arXiv  Detail & Related papers  (2023-06-19T22:53:42Z)

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.