CacheDyG introduces a cache‑refine framework that decouples temporal propagation from parameter updates in dynamic graph neural networks. By storing graph‑aware node‑time representations in non‑trainable buffers and updating only a lightweight refiner, residual gate, and link predictor during training, it reduces repeated recomputation of historical structures. Experiments on five benchmarks show that CacheDyG uses fewer trainable parameters, runs faster, and achieves competitive or better predictive performance compared to existing baselines.
By PinHeng Zong, Ye Yuan
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
arXiv:2609.25781v1 Announce Type: new
Abstract: Graph Incremental Learning has garnered increasing attention as dynamic graph data continues to emerge across diverse fields. Conventional approaches p...
By Zihan Mei, Zhili Qin, Tongze Zhang, Hongyuan Liu, Junming Shao, Qinli Yang
Generalizable dynamic graph anomaly detection (DGAD) enables pretrained detectors to identify anomalies in unseen target domains without costly retraining. However, existing methods often fail for two reasons.
SiST‑GNN introduces a simultaneous spatial‑temporal message‑passing framework for dynamic graph neural networks, fusing per‑node temporal embeddings with spatial aggregation in a single operation. By maintaining a recurrent hidden state per node and treating it as a cross‑time edge, the model jointly reasons over topology and evolution. Experiments on link‑prediction and node‑classification benchmarks show significant improvements over prior methods, achieving up to 158% gains in live‑update link prediction and outperforming discrete‑time baselines by 7–23% in dynamic node classification.
By Shubhajit Roy, Anirban Dasgupta
arXiv:2608. 09016v1 Announce Type: cross Abstract: Node ranking is a fundamental problem in graph information retrieval, measuring the relative importance of nodes and supporting a wide range of applications such as influence analysis, recommendation, and graph-based retrieval augmented generation.
By Lujie Ban, Jiasheng shi, Yingli Zhou, Kaiwen Xue, Daiyin Wang, Xubin Li, Shuanghua Li, Chenhao Ma
arXiv:2609.05955v1 Announce Type: new
Abstract: Tabular foundation models have become powerful graph learners. Systems such as G2T-FM and GraphPFN encode each node as a feature row and make predictio...
By Mingqi Yang, Zidong Guo, Jihui Yang, Wenming Zuo
arXiv:2608.28237v1 Announce Type: new
Abstract: Predictive Process Monitoring (PPM) models are increasingly deployed in dynamic environments where concept drift causes the underlying process distribu...
By Sjoerd van Straten, Marwan Hassani
arXiv:2605. 12998v3 Announce Type: replace Abstract: Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting.
By Guiquan Sun, Xikun Zhang, Jingchao Ni, Dongjin Song
arXiv:2609.39673v1 Announce Type: new
Abstract: Can a pretrained graph model replace training and tuning a separate predictor for each dataset? Answering this requires evaluating prediction quality a...
By Jinmo Lee, Dooho Lee, Minho Jeong, Jaemin Yoo
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.
By Alexander Panyshev, Dmitry Vinichenko, Oleg Travkin, Roman Alferov, Alexey Zaytsev
arXiv:2608.29588v1 Announce Type: new
Abstract: Reasoning over text-attributed graphs (TAGs) requires large language models (LLMs) to combine a node's text with evidence distributed across its neighb...
By Yilun Liu, Boyu Luo, Yanran Tang, Ruihong Qiu, Zi Huang