arXiv Machine Learning

CoPE-CGL: Community-aware Prototype Extraction for Replay-Free Continual Graph Learning

CoPE-CGL is a replay‑free continual graph learning framework that uses community‑aware prototype extraction and a parameter‑free structural prior derived from multi‑hop Laplacian smoothing. It adapts to both transductive and inductive settings, requiring only a constant amount of storage per task and no past graph data. In experiments on Arxiv, CoraFull, and other benchmarks, CoPE‑CGL achieves performance comparable to or better than replay baselines, runs significantly faster, and eliminates catastrophic forgetting.

arXiv Machine Learning

Oct 8

HCPN-GCN: Scaling Hierarchical Prototype Networks with Cone Geometry for Continual Graph Learning

HCPN-GCN is a graph-aware extension of Hierarchical Prototype Networks that replaces linear feature extractors with Graph Convolutional Networks and introduces cone-based prototypes with a diversity regularization objective. This design yields richer graph-aware representations, reduces prototype proliferation, and maintains near-zero forgetting. Experiments on six continual graph learning benchmarks show consistent accuracy improvements over the original HPN and other baselines while using roughly 30× fewer atomic prototypes.

By Sammuel R. Silva, Vander L. S. Freitas, Gladston Moreira, Eduardo J. S. Luz, Rodrigo Silva

rag benchmarks

arXiv Machine Learning

Oct 16

HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning

HERO is a heterogeneous continual graph learning framework that preserves historical knowledge at both structural and semantic levels while adapting to new tasks. It uses DiSCo to select representative target nodes and reconstruct compact heterogeneous contexts, and applies multi-level knowledge distillation to align task-conditioned predictions with semantic responses. Experiments on four datasets with three HGNN backbones show HERO achieves best or tied-best average performance while maintaining competitive forgetting.

By Guiquan Sun, Xikun Zhang, Jingchao Ni, Dongjin Song

efficiency benchmarks

arXiv Machine Learning

Oct 15

MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning

arXiv:2610.20238v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially. In class-incremental g...

By Junlin Chen, Wenhao Wang, Junchao Xu, Qingsong Yao, Fei Xue, Li Sun, Xiao Wang, Ruijie Wang

fine-tuning 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

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 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 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

Jun 11

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs

arXiv:2606. 11562v1 Announce Type: new Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood.

By Zhuoyi Peng, Jingzhou Jiang, Hanlin Gu, Lixin Fan, Yi Yang

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jul 14

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.

By Tingxu Yan Ye Yuan

arXiv AI

Jul 13

HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning

arXiv:2607. 08784v1 Announce Type: cross Abstract: Federated continual learning (FCL) evaluates how distributed clients learn from changing data streams while retaining previously learned knowledge.

By Thinh T. H. Nguyen, Le-Tuan Nguyen, Minh-Duong Nguyen, Nhi Trinh, Anh Tran Nam Nguyet, Dung D. Le, Kok-Seng Wong

benchmarks

arXiv Machine Learning

Aug 12

GLAM: Efficient Continual Learning at Scale via Grouped LoRA Adapter Merging

arXiv:2509. 13211v4 Announce Type: replace Abstract: The ability to learn continuously over time remains a major challenge for modern machine learning systems, even in the era of Foundation Models.

By Irene Testa, Luigi Quarantiello, Eric Nuertey Coleman, Samrat Mukherjee, Julio Hurtado, Vincenzo Lomonaco

fine-tuning benchmarks

arXiv Machine Learning

Oct 5

Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning

arXiv:2605.10247v2 Announce Type: replace Abstract: Applying Large Language Models (LLMs) to graph-structured data usually involves multi-step pipelines in which textual node attributes are compresse...

By Dario Vajda

llms fine-tuning benchmarks

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