发表机构
Beihang University（北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对图小样本类增量学习中的过拟合与表示漂移问题，提出MAGIC框架，利用拓扑先验与解析式蒸馏，在多个数据集上显著提升准确率并降低训练时间。
AI 中文摘要
图小样本类增量学习（GFSCIL）要求模型在仅从少量标记节点中持续识别新出现的类别的同时，保留先前获得的知识。除了传统图持续学习固有的灾难性遗忘之外，GFSCIL还面临两个独特的挑战：极其有限的新类监督导致严重的过拟合，而跨会话边——连接新到达节点与历史节点的边——会改变历史传播邻域，从而引发表示漂移。我们提出了MAGIC，一个无重放的GFSCIL框架，它将冻结的图表示骨干网络（例如，本质上无参数的骨干如SGC或预训练的图基础模型）与闭式解析式持续学习相结合。为了缓解新会话的过拟合，MAGIC从基础图中学习一个拓扑先验，该先验能够刻画同质性和异质性关系，并通过Potts马尔可夫随机场推断注入该先验，以细化新类别的监督。为了减轻表示漂移，MAGIC通过漂移感知的解析式蒸馏，将受影响历史节点的旧表示上的先前预测迁移到其更新后的表示上。在五个数据集和八个基线上的实验证明了MAGIC的有效性。在5-shot设置下，与最佳基线相比，MAGIC的平均准确率和最终准确率分别平均提高了5.48个百分点和9.33个百分点，并将性能下降平均降低了10.78个百分点。MAGIC在1-shot和3-shot设置下也显示出明显优势，且随着支持样本数量的增加，增益更大。此外，MAGIC所需的训练时间大幅减少。
英文摘要
Graph few-shot class-incremental learning (GFSCIL) requires a model to continually recognize emerging classes from only a few labeled nodes while preserving previously acquired knowledge. Beyond the catastrophic forgetting inherited from conventional graph continual learning, GFSCIL presents two distinctive challenges: extremely limited novel-class supervision causes severe overfitting, while cross-session edges---edges connecting newly arriving nodes with historical nodes---alter historical propagation neighborhoods and thereby induce representation drift. We propose MAGIC, a replay-free GFSCIL framework that combines a frozen graph representation backbone (e.g., an intrinsically parameter-free backbone such as SGC or a pretrained graph foundation model) with closed-form analytic continual learning. To alleviate novel-session overfitting, MAGIC learns a topological prior from the base graph that can characterize both homophilous and heterophilous relations, and injects this prior through Potts Markov random field inference to refine supervision for novel classes. To mitigate representation drift, MAGIC transfers previous predictions from the old representations of affected historical nodes to their updated representations through drift-aware analytic distillation. Experiments across five datasets and eight baselines demonstrate the effectiveness of MAGIC. Under the 5-shot setting, MAGIC improves Mean Accuracy and Final Accuracy by 5.48 percentage points and 9.33 percentage points on average, and reduces Performance Drop by 10.78 percentage points on average compared with the best baselines. MAGIC also shows clear advantages under the 1- and 3-shot settings, with larger gains as the number of supports increases. Moreover, MAGIC requires substantially less training time.
