Aug 2026· Data mining and knowledge discovery· Vol 40· 0 citations· 51 references
Advanced Graph Neural Networks
TL;DR
This study introduces a novel memory-augmented self-learning framework that extracts and provides diverse learning sources for adaptive knowledge distillation from the student model itself, resulting in a 2.5-6% increase in accuracy across various benchmark datasets compared to current GNN training and self-distillation methods.
This work proposes a student design based on simple, homogeneous blocks mirroring those of the teacher, distilling knowledge between corresponding blocks, showing that intermediate block-wise distillation, guided appropriately, is key to building compact data-efficient models without sacrificing accuracy.
Irene Trigueros-Lorca, Leonardo Concepción, Christian Wagner et al.· 0 citations
A multimodal model that combines two complementary ideas: a self-supervised method that enables a GNN encoder pretrained on one dataset to operate directly on another dataset with a different node-feature dimensionality, without rebuilding the model or realigning the data is investigated.
Fumiaki Kimino, Ryoma Sato Sokendai, National Institute of Informatics· 1 citation
Continual learning requires a model to retain knowledge of old tasks while sequentially learning new tasks, but standard neural networks typically suffer from catastrophic forgetting in this setting. To address this challenge, a new method is proposed based on a learnable knowledge transfer network. Specifically, a tra...
Han Ju· International Conference on...· 0 citations
This paper proposes a novel lightweight plastic-memory framework specifically designed for few-shot incremental learning on graphs, featuring an innovative evolving micro-clustering structure that dynamically updates representations of class prototypes, sub-prototypes, and their interaction weights.
Zi-Han Mei, Zhi-Li Qin, Tongze Zhang et al.· 0 citations
Curriculum Learning (CL) orders training samples from easy to hard, but its application to graphs remains limited: existing methods rely on architecture-specific signals such as training loss, or on hand-crafted structural heuristics that do not transfer across datasets. Standard Graph Neural Network (GNN) training sid...
Dulara Madhusanka, Sanka Mohottala, A. Lindamulage et al.· Moratuwa Engineering Researc...· 0 citations
Knowledge distillation aims to improve the performance of lightweight student models by transferring knowledge from larger and more powerful teacher models. However, a substantial size gap between teacher and student models often impedes effective knowledge transfer. Most existing approaches adopt a static, one-way tea...
Da-Wen Jiang, Zhi-Shu Shen, Ze-Yu Liu et al.· 0 citations
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