← Back to all news
arXiv Machine Learning By Bo-Young Lim, Seokhwan Kim, Kisung Lee, Hyuk-Yoon Kwon

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

Read the original on arXiv Machine 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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
ragbenchmarks
More like this →
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
efficiencybenchmarks
More like this →
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-tuningbenchmarkssafety
More like this →