  Graph convolutional networks (GCNs) have been employed as a kind of
significant tool on many graph-based applications recently. Inspired by
convolutional neural networks (CNNs), GCNs generate the embeddings of nodes by
aggregating the information of their neighbors layer by layer. However, the
high computational and memory cost of GCNs due to the recursive neighborhood
expansion across GCN layers makes it infeasible for training on large graphs.
To tackle this issue, several sampling methods during the process of
information aggregation have been proposed to train GCNs in a mini-batch
Stochastic Gradient Descent (SGD) manner. Nevertheless, these sampling
strategies sometimes bring concerns about insufficient information collection,
which may hinder the learning performance in terms of accuracy and convergence.
To tackle the dilemma between accuracy and efficiency, we propose to use
aggregators with different granularities to gather neighborhood information in
different layers. Then, a degree-based sampling strategy, which avoids the
exponential complexity, is constructed for sampling a fixed number of nodes.
Combining the above two mechanisms, the proposed model, named Mix-grained GCN
(MG-GCN) achieves state-of-the-art performance in terms of accuracy, training
speed, convergence speed, and memory cost through a comprehensive set of
experiments on four commonly used benchmark datasets and a new Ethereum
dataset.
