arXiv Machine Learning

Never Skip a Batch: Dense Learning of Temporal GNNs via Adaptive Pseudo-Supervision

arXiv:2505. 12526v2 Announce Type: replace Abstract: Temporal graph networks suffer from irregular supervision in realworld dynamic graphs, as most minibatches contain few labeled events.

arXiv Machine Learning

Jul 7

FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks

arXiv:2504. 07337v2 Announce Type: replace Abstract: Aggregating temporal signals from historic interactions is a key step in future link prediction on dynamic graphs.

By Or Feldman, Krishna Sri Ipsit Mantri, Carola-Bibiane Sch\"onlieb, Chaim Baskin, Moshe Eliasof

benchmarks

arXiv Machine Learning

Jul 22

Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs

arXiv:2607. 18412v1 Announce Type: new Abstract: Dynamic graph learning aims to capture evolving structural and semantic patterns in real-world systems, such as fraud detection and recommender systems.

By Huizhe Zhang, Yuchang Zhu, Huazhen Zhong, Liang Chen, Zibin Zheng

rag

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

Jun 10

Temporal Sheaf Neural Networks with Dynamic Orthogonal Transport

arXiv:2606. 10071v1 Announce Type: cross Abstract: We introduce Temporal Sheaf Neural Networks (TSNN), a temporal link prediction framework that equips each node with a time-varying orthogonal frame and compares node states only after explicit transport between local coordinate systems.

By Md Sadek Hossain Asif, Tanzila Khan, Md. Mosaddek Khan

rag diffusion benchmarks

arXiv Machine Learning

5d ago

Incremental Evaluation and Training in Relational Deep Learning

arXiv:2608. 13023v1 Announce Type: new Abstract: Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning.

By Jakub Pele\v{s}ka, Gustav \v{S}\'ir

fine-tuning benchmarks

arXiv Machine Learning

Aug 10

Edge Sparsification via Temporal Forman-Ricci Curvature for Dynamic Graph Learning

arXiv:2608. 07158v1 Announce Type: new Abstract: Temporal graph learning has become essential for analyzing real-world systems whose interactions continuously evolve over time, including financial transaction networks, communication systems, and online social platforms.

By Poupak Azad, Cuneyt Gurcan Akcora, Kiarash Shamsi

benchmarks

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

Jul 7

FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training

arXiv:2607. 05095v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) are widely used for learning from dynamic graphs in applications such as recommendation, social network analysis, and traffic forecasting.

By Yushu Cai, Qingrui Zhu, Lei Liu, Kai Sheng, Hao Chen, Xin He

benchmarks

arXiv Machine Learning

Jun 30

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting

arXiv:2505. 16903v2 Announce Type: replace Abstract: Prompt tuning has become a key mechanism for adapting pre-trained Graph Neural Networks (GNNs) to new downstream tasks.

By Peyman Baghershahi, Sourav Medya

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 5

Pretraining Recurrent Networks without Recurrence

arXiv:2606. 06479v1 Announce Type: new Abstract: Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations.

By Akarsh Kumar, Phillip Isola

llms

arXiv Machine Learning

Jul 8

Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift

arXiv:2607. 05908v1 Announce Type: new Abstract: Real-world data distributions evolve over time, inducing temporal distribution shift that can substantially degrade the reliability of deployed machine learning systems.

By Robin Holzinger (Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA), Riccardo Colletti (Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA)

llms computer-vision nlp

arXiv Machine Learning

Aug 12

ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models

arXiv:2608. 10120v1 Announce Type: new Abstract: Modern sequence models, from Transformers to State Space Models, have enabled powerful generative modeling across diverse domains, yet they are typically trained to predict what happens while treating when it happens as a secondary concern.

By Adrien Schoen, Nachiketa Ratnakar Patil, Arjun Bhagoji, Francesco Bronzino

llms

Related stories