TAAM：基于任务感知自适应调制的归纳图类增量学习
TAAM:Inductive Graph-Class Incremental Learning with Task-Aware Adaptive Modulation
AI总结：
TAAM通过任务感知自适应调制模块实现高效的图类增量学习，避免灾难性遗忘并提升模型性能。
摘要：
图持续学习（GCL）旨在解决流式图数据的挑战。然而，当前方法往往依赖于回放策略，这引发了内存限制和隐私问题等担忧，同时难以解决稳定性与可塑性困境。在本文中，我们提出轻量级、任务特定的模块可以有效引导固定GNN主干的推理过程。基于这一想法，我们提出了任务感知自适应调制（TAAM）。TAAM的关键组件是其轻量级神经突触调制器（NSMs）。对于每个新任务，一个专用的NSM被训练并冻结，充当一个
英文摘要：
Graph Continual Learning (GCL) aims to solve the challenges of streaming graph data. However, current methods often depend on replay-based strategies, which raise concerns like memory limits and privacy issues, while also struggling to resolve the stability-plasticity dilemma. In this paper, we suggest that lightweight, task-specific modules can effectively guide the reasoning process of a fixed GNN backbone. Based on this idea, we propose Task-Aware Adaptive Modulation (TAAM). The key component of TAAM is its lightweight Neural Synapse Modulators (NSMs). For each new task, a dedicated NSM is trained and then frozen, acting as an "expert module." These modules perform detailed, node-attentive adaptive modulation on the computational flow of a shared GNN backbone. This setup ensures that new knowledge is kept within compact, task-specific modules, naturally preventing catastrophic forgetting without using any data replay. Additionally, to address the important challenge of unknown task IDs in real-world scenarios, we propose and theoretically prove a novel method named Anchored Multi-hop Propagation (AMP). Notably, we find that existing GCL benchmarks have flaws that can cause data leakage and biased evaluations. Therefore, we conduct all experiments in a more rigorous inductive learning scenario. Extensive experiments show that TAAM comprehensively outperforms state-of-the-art methods across eight datasets. Code and Datasets are available at: https://github.com/1iuJT/TAAM_AAMAS2026.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
