  Graph neural networks (GNN) are powerful models for many graph-structured
tasks. Existing models often assume that the complete structure of the 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 bridge GNN and lifelong learning by
converting a continual graph learning problem to a regular graph learning
problem so GNN can inherit the lifelong learning techniques developed for
convolutional neural networks (CNN). We propose a new topology, the feature
graph, which takes features as new nodes and turns nodes into independent
graphs. This successfully converts the original problem of node classification
to graph classification. 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 method to bridge graph learning and lifelong learning via a novel graph
topology. Source code is available at https://github.com/wang-chen/LGL
