Graph Few-shot Class-incremental Learning

TL;DR

This paper introduces a novel graph meta-learning framework, HAG-Meta, for few-shot class-incremental learning on graph data, effectively balancing stability and plasticity in classifying new and old classes with limited data.

Contribution

It proposes a Graph Pseudo Incremental Learning paradigm and a Hierarchical-Attention-based Graph Meta-learning framework, HAG-Meta, to improve incremental learning on graph-structured data.

Findings

HAG-Meta outperforms baseline and state-of-the-art methods on three real-world datasets.

The framework effectively balances stability of old knowledge and adaptability to new classes.

Extensive experiments demonstrate significant improvements in incremental learning tasks.

Abstract

The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems. A large portion of high-impact applications like social media, recommendation systems, E-commerce platforms, etc. can be represented by graph models. In this paper, we investigate the challenging yet practical problem, Graph Few-shot Class-incremental (Graph FCL) problem, where the graph model is tasked to classify both newly encountered classes and previously learned classes. Towards that purpose, we put forward a Graph Pseudo Incremental Learning paradigm by sampling tasks recurrently from the base classes, so as to produce an arbitrary number of training episodes for our model to practice the incremental learning skill. Furthermore, we design a Hierarchical-Attention-based Graph Meta-learning framework, HAG-Meta. We present a task-sensitive regularizer calculated from task-level attention and node class prototypes to mitigate overfitting onto either novel or base classes. To employ the topological knowledge, we add a node-level attention module to adjust the prototype representation. Our model not only achieves greater stability of old knowledge consolidation, but also acquires advantageous adaptability to new knowledge with very limited data samples. Extensive experiments on three real-world datasets, including Amazon-clothing, Reddit, and DBLP, show that our framework demonstrates remarkable advantages in comparison with the baseline and other related state-of-the-art methods.