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

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 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

arXiv Machine Learning

Jul 28

Gradient-Free Continual Learning

arXiv:2504. 01219v2 Announce Type: replace Abstract: Neural networks are notorious for forgetting old skills when taught new ones - a problem known as catastrophic forgetting.

By Grzegorz Rype\'s\'c

benchmarks safety

arXiv AI

6d ago

EvoGraph-Mem: Failure-Aware Editable Graph Memory for Long-Term Language Agents

arXiv:2608. 11248v1 Announce Type: new Abstract: Long-term memory is essential for language agents operating across extended interactions and evolving tasks.

By Yuxi Qian, Yuxiang Ren

agents 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 AI

6d ago

Representation Finetuning for Continual Learning

arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.

By Haihua Luo, Xuming Ran, Tommi K\"arkk\"ainen, Huiyan Xue, Zhonghua Chen, Qi Xu, Fengyu Cong

fine-tuning benchmarks

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