Lifelong Graph Learning

Abstract

Graph neural networks (GNNs) are powerful models for many graph-structured tasks. Existing models often assume that a complete structure of a graph is available during training. In practice, however, graph-structured data is usually formed in a streaming fashion so that learning a graph continuously is often necessary. In this paper, we aim to bridge GNN to lifelong learning by converting a graph problem to a regular learning problem, so that GNN can inherit the lifelong learning techniques developed for convolutional neural networks (CNNs). To this end, we propose a new graph topology based on feature cross-correlation, namely, the feature graph. It takes features as new nodes and turns nodes into independent graphs. This successfully converts the original problem of node classification to graph classification, in which the increasing nodes are turned into independent training samples. In the experiments, we demonstrate the efficiency and effectiveness of feature graph networks (FGN) by continuously learning a sequence of classical graph datasets. We also show that FGN achieves superior performance in two applications, i.e., lifelong human action recognition with wearable devices and feature matching. To the best of our knowledge, FGN is the first work to bridge graph learning to lifelong learning via a novel graph topology.

1.. Introduction

Graph neural networks (GNN) have received increasing attention and proved useful for many tasks with graphstructured data, such as citation, social, and protein networks [52] . However, graph data is sometimes formed in a streaming fashion and real-world datasets are continuously evolving over time, thus learning a streaming graph is expected in many cases [46] . For example, in a social network, the number of users often grows over time and we expect that the model can learn continuously with new users. In this paper, we extend graph neural networks to lifelong learning, which is also known as continual or incremental learning [26] . Lifelong learning often suffers from "catastrophic forgetting" if the models are simply updated with new samples [35] . Although some strategies have been developed to alleviate the forgetting problem for convolutional neural networks (CNN), they are still difficult for graph networks. This is because in the lifelong learning setting, the graph size can increase over time and we have to drop off old data or samples to learn new knowledge. However, the existing graph model cannot directly overcome this difficulty. For example, graph convolutional networks (GCN) require the entire graph for training [20] . SAINT [58] requires pre-processing for the entire dataset. Sampling strategies [7, 13, 58] easily forget old knowledge when learning new knowledge. Recall that regular CNNs are trained in a mini-batch manner where the model can take samples as independent inputs [23] . Our question is: can we convert a graph task into a traditional CNN-like classification problem, so that (I) nodes can be predicted independently and (II) the lifelong learning



(a) Regular graph G.

Feature graph G F .

Figure1. We introduce feature graph network (FGN) for lifelong graph learning. A feature graph takes the features as nodes and turns nodes into graphs, resulting in a graph predictor instead of the node predictor. This makes the lifelong learning techniques for CNN applicable to GNN, as the new nodes in a regular graph become individual training samples. Take the node a with label za in the regular graph G as an example, its features xa = [1, 0, 0, 1] are nodes {a1, a2, a3, a4} in feature graph G F a . The feature adjacency is established via feature cross-correlation between a and its neighbors N (a) = {a, b, c, d, e} to model feature "interaction."

