arXiv Machine Learning

CA-DGCL: Dynamic Graph Continual Learning via Condensation and Attachment

arXiv:2607. 11112v1 Announce Type: new Abstract: Dynamic graph continual learning (DGCL) is an effective manner for handling catastrophic forgetting in dynamic graphs.

arXiv AI

Jul 14

UNIT: Unleash Large Language Models Potential for Graph Continual Learning

arXiv:2607. 10159v1 Announce Type: new Abstract: In real-world multimodal web scenarios, graph-structured data often arrives in a streaming manner, making graph continual learning a crucial paradigm for continuously modeling such evolving structures.

By Tairan Huang, Yili Wang, Beibei Hu, Yiting Shi, Qiutong Li, Changlong He, Jianliang Gao

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 31

FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

arXiv:2607. 27665v1 Announce Type: new Abstract: Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information.

By Zekai Chen, Haodong Lu, Shihao Li, Weiwei Ji, Xunkai Li, Xun Wu, Yinlin Zhu, Rong-Hua Li

efficiency multimodal

arXiv AI

Jun 16

DYNA : Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning

arXiv:2606. 15778v1 Announce Type: cross Abstract: Large Language Models (LLMs) struggle to incorporate new knowledge without forgetting or costly retraining.

By Ali Sarabadani, Mahtab Tajvidiyan

llms rag fine-tuning

arXiv Machine Learning

Sep 17

Leveraging Complementary Embeddings for Replay Selection in Continual Learning with Small Buffers

The paper introduces Multiple Embedding Replay Selection (MERS), a graph‑based method that combines supervised and self‑supervised embeddings to improve sample selection for replay buffers in continual learning. MERS replaces traditional buffer selection modules and demonstrates consistent performance gains over state‑of‑the‑art strategies, especially in low‑memory settings. Experiments on CIFAR‑100 and TinyImageNet show that MERS outperforms single‑embedding baselines without adding model parameters or increasing replay volume, making it a practical, drop‑in enhancement for replay‑based continual learning.

By Danit Yanowsky, Daphna Weinshall

rag benchmarks

arXiv Machine Learning

Sep 23

A Lightweight Plastic-Memory Framework for Graph Few-Shot Class-Incremental Learning

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

efficiency benchmarks

arXiv Machine Learning

Jul 2

SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport

arXiv:2607. 00377v1 Announce Type: new Abstract: Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision - a paradigm that has attracted widespread attention.

By Yuting Zhang, Yanbei Liu, Zhitao Xiao, Lei Geng, Yanwei Pang, Xiao Wang

rag efficiency benchmarks

arXiv AI

4d ago

HiTS-CL: A Continual Learning Framework for Long-Horizon Temporal Knowledge Graph Extrapolation

arXiv:2609.36559v1 Announce Type: cross Abstract: Extrapolative temporal knowledge graph reasoning (TKGR) predicts future facts from historical snapshots. Most existing methods train once on an early...

By Yansong Liu, Rui Liu, Yuan Zuo, Hongwei Zhao, Da Fu, Fuwei Zhang, Fuzhen Zhuang, Yong Chen, Zhe Li

fine-tuning efficiency benchmarks

arXiv Machine Learning

Sep 23

CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning

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

benchmarks

arXiv Machine Learning

Jun 2

G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs

arXiv:2606. 01873v1 Announce Type: new Abstract: LLM-as-Aligner has emerged as a prevalent pre-training paradigm for Text-Attributed Graphs(TAGS), aligning graph and text modalities into a shared embedding space via CLIP-style contrastive learning.

By Yuhan Wang, Yibo Ding, Yutong Ye, Mufan Zhao, Wenbo Zhang, Ruijie Wang, Jianxin Li

llms rag fine-tuning multimodal benchmarks safety

arXiv AI

Jun 16

AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning

arXiv:2512. 18295v2 Announce Type: replace-cross Abstract: Continual graph learning (CGL) aims to enable graph neural networks to incrementally learn from a stream of graph structured data without forgetting previously acquired knowledge.

By Xuling Zhang, Jindong Li, Yifei Zhang, Mingqi Yang, Menglin Yang

benchmarks safety

arXiv Machine Learning

Jun 30

DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts

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

benchmarks

arXiv Machine Learning

Jun 25

A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction

arXiv:2602. 14239v3 Announce Type: replace-cross Abstract: Predicting links in sparse, continuously evolving networks is a central challenge in network science.

By Nafiseh Sadat Sajadi, Behnam Bahrak, Mahdi Jafari Siavoshani

rag efficiency

Related stories