arXiv Machine Learning

Unlearning on Spatio-Temporal Graphs through Subgraph Virtual Edge Reconstruction

arXiv Machine Learning

Sep 1

Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning

arXiv:2608.29360v1 Announce Type: new Abstract: Spatiotemporal graphs underpin applications such as traffic forecasting, weather forecasting, and healthcare monitoring. Privacy regulations such as th...

By Qiming Guo, Wenbo Sun, Ye Wang, Wenlu Wang

benchmarks safety

arXiv Machine Learning

Jul 17

What Do Temporal Graph Learning Models Learn?

arXiv:2510. 09416v4 Announce Type: replace Abstract: Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models.

By Abigail J. Hayes, Tobias Schumacher, Markus Strohmaier

benchmarks safety

arXiv Machine Learning

Aug 6

EvtGraph: Event-Adaptive Compression for Sparse Temporal Graph Learning in Multimodal Time Series

arXiv:2608. 04368v1 Announce Type: new Abstract: Multimodal temporal data are inherently irregular and uneven in information density, yet most models rely on uniform discretization, leading to inefficient representations.

By Ziqian Wang, Tingxiong Xiao, Yuxiao Cheng, Jinli Suo

llms multimodal benchmarks

arXiv AI

Jun 11

From Uniform to Learned Graph Priors: Diffusion for Structure Discovery

arXiv:2606. 11831v1 Announce Type: cross Abstract: Neural relational inference (NRI) methods discover interaction graphs from trajectories through variational reasoning on discrete potential edges.

By Qi Shao, Hao Guo, Jiawen Chen, Duxin Chen, Wenwu Yu

diffusion benchmarks

arXiv Machine Learning

Jun 2

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks

arXiv:2606. 01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs).

By Zhongyue Zhang, Guangyin Jin, Yuxuan Liang, Suwan Yin, Yuankai Wu

arXiv Machine Learning

Jun 3

TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer

arXiv:2506. 00431v2 Announce Type: replace Abstract: Due to the proficiency of self-attention mechanisms (SAMs) in capturing dependencies in sequence modeling, several existing dynamic graph neural networks (DGNNs) utilize Transformer architectures with various encoding designs to capture sequential evolutions of dynamic graphs.

By Jie Peng, Zhewei Wei, Yuhang Ye

llms rag benchmarks safety

arXiv Machine Learning

Jun 26

Graph Neural Networks Applications Across Domains: All Insights You Need

arXiv:2606. 27202v1 Announce Type: new Abstract: Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure.

By Abderaouf Bahi

computer-vision safety

arXiv AI

Sep 24

QUARTET: Quad-branch cross-Attention and Random-walk Traces for Enhancing Transformers on Relational Graphs

arXiv:2609.26855v1 Announce Type: cross Abstract: Relational Deep Learning (RDL) models multi-table databases as heterogeneous temporal graphs, and graph transformers currently achieve state-of-the-a...

By Kyaw Hpone Myint, Nan Jiang, Xiang Li, Zhe Wu, Alexandre G. R. Day, Pranab Mohanty, Giri Iyengar

llms benchmarks

arXiv Machine Learning

Jun 19

Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift

arXiv:2604. 15838v2 Announce Type: replace Abstract: Distribution shift severely degrades the performance of deep forecasting models.

By Zhaobo Hu, Vincent Gauthier, Mehdi Naima

diffusion

arXiv Machine Learning

Jun 2

Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection

arXiv:2606. 00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes.

By Yilin Liu, Hongchao Zhang, Taylor T. Johnson, Ahmad F. Taha, Meiyi Ma

benchmarks

arXiv AI

Jun 24

Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts

arXiv:2405. 19062v2 Announce Type: replace-cross Abstract: Continuous-Time Dynamic Graphs (CTDGs) enable fine-grained modeling of evolving relational systems.

By Lanting Fang, Yulian Yang, Yawei Zhang, Shanshan Feng, Kaiyu Feng, Hanning Yuan

arXiv AI

Jul 10

Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution

arXiv:2607. 07716v1 Announce Type: cross Abstract: Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy.

By Yazheng Liu, Xi Zhang, Sihong Xie, Hui Xiong

benchmarks

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