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

• URL: http://arxiv.org/abs/2602.08036v1
• Date: Sun, 08 Feb 2026 16:11:55 GMT
• Title: TAAM:Inductive Graph-Class Incremental Learning with Task-Aware Adaptive Modulation
• Authors: Jingtao Liu, Xinming Zhang,
• Abstract summary: We propose Task-Aware Adaptive Modulation (TAAM) to guide the reasoning process of a fixed GNN backbone.<n>For each new task, a dedicated NSM is trained and then frozen, acting as an "expert module"<n>These modules perform detailed, node-attentive adaptive modulation on the computational flow of a shared GNN backbone.
• Score: 3.780931979011216
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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