TAAM: Inductive Graph-Class Incremental Learning with Task-Aware Adaptive Modulation

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 stabilityplasticity dilemma. In this paper, we suggest that lightweight, taskspecific 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.

Authors

• Jingtao Liu University of Science and Technology of China
• Xinming Zhang University of Science and Technology of China

Keywords

• Continual Graph Learning
• Graph representation learning
• Inductive Learning
• Multiple Experts
• Graph neural network