  Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep
learning model for representation learning on graphs. However, it remains
notoriously challenging to train and inference GCNs over large graph datasets,
limiting their application to large real-world graphs and hindering the
exploration of deeper and more sophisticated GCN graphs. This is because as the
graph size grows, the sheer number of node features and the large adjacency
matrix can easily explode the required memory and data movements. To tackle the
aforementioned challenges, we explore the possibility of drawing lottery
tickets when sparsifying GCN graphs, i.e., subgraphs that largely shrink the
adjacency matrix yet are capable of achieving accuracy comparable to or even
better than their full graphs. Specifically, we for the first time discover the
existence of graph early-bird (GEB) tickets that emerge at the very early stage
when sparsifying GCN graphs, and propose a simple yet effective detector to
automatically identify the emergence of such GEB tickets. Furthermore, we
advocate graph-model co-optimization and develop a generic efficient GCN
early-bird training framework dubbed GEBT that can significantly boost the
efficiency of GCN training by (1) drawing joint early-bird tickets between the
GCN graphs and models and (2) enabling simultaneously sparsification of both
the GCN graphs and models. Experiments on various GCN models and datasets
consistently validate our GEB finding and the effectiveness of our GEBT, e.g.,
our GEBT achieves up to 80.2% ~ 85.6% and 84.6% ~ 87.5% savings of GCN training
and inference costs while offering a comparable or even better accuracy as
compared to state-of-the-art methods. Our source code and supplementary
appendix are available at https://github.com/RICE-EIC/Early-Bird-GCN.
