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

TL;DR

TAAM introduces a task-aware, modular approach for graph continual learning that avoids replay, addresses unknown task IDs, and outperforms existing methods on multiple datasets.

Contribution

The paper proposes TAAM, a novel GCL method using task-specific neural modulators and a theoretical solution for unknown task IDs, improving stability and privacy.

Findings

Outperforms state-of-the-art on eight datasets

Effectively prevents catastrophic forgetting without replay

Addresses unknown task IDs with theoretical guarantees

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.