一种用于图少样本类增量学习的轻量级塑性记忆框架
A Lightweight Plastic-Memory Framework for Graph Few-Shot Class-Incremental Learning
- University of Electronic Science and Technology of China（电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出一种轻量级塑性记忆框架，通过演化微聚类结构动态更新类原型，结合记忆驱动的元学习，解决图少样本类增量学习中的灾难性遗忘，并在四个基准数据集上验证了其优越性。
中文摘要 AI 辅助
随着动态图数据在不同领域的持续涌现，图增量学习日益受到关注。传统方法主要通过回放或蒸馏技术保留节点相关知识来应对灾难性遗忘；然而，这些方法往往带来高昂的计算成本和低效率。在真实场景中，新类别的标记数据稀缺，这一问题进一步加剧。本文提出了一种新颖的轻量级塑性记忆框架，专门用于图上的少样本增量学习。我们框架的核心思想是构建一个随时间演化的塑性记忆模块，该模块不断更新和扩展其记忆，以容纳新类别，同时保留先前学到的知识。与现有技术相比，我们的记忆模块既轻量又有效，具有创新的演化微聚类结构，可动态更新类原型、子原型及其交互权重的表示。在此记忆模块的基础上，我们引入了一个记忆驱动的元学习框架，该框架在内循环中增强对新任务的适应性，同时在外循环中保持对早期任务的稳定性。在四个基准数据集上的大量实验表明，该框架在平衡旧知识的稳定性和新知识的适应性方面具有优越性能。
英文摘要
Graph Incremental Learning has garnered increasing attention as dynamic graph data continues to emerge across diverse fields. Conventional approaches primarily address catastrophic forgetting by preserving node-related knowledge through replay or distillation techniques; however, they often incur high computational costs and inefficiency. This issue is further exacerbated in real-world scenarios where labeled data for new classes is scarce. In this paper, we propose a novel lightweight plastic-memory framework specifically designed for few-shot incremental learning on graphs. The core idea of our framework is the construction of a plastic-memory module that evolves over time, continuously updating and expanding its memory to accommodate new classes while retaining previously learned knowledge. In contrast to existing techniques, our memory module is both lightweight and effective, featuring an innovative evolving micro-clustering structure that dynamically updates representations of class prototypes, sub-prototypes, and their interaction weights. Building on this memory module, we introduce a memory-driven meta-learning framework that enhances adaptability to new tasks in its inner loop while maintaining stability for earlier tasks in the outer loop. Extensive experiments on four benchmark datasets demonstrate the framework's superior performance in balancing stability for old knowledge and adaptability to new knowledge.
