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Graph convolutional networks (GCNs) have been employed as a kind ofsignificant tool on many graph-based applications recently. Inspired byconvolutional neural networks (CNNs), GCNs generate the embeddings of nodes byaggregating the information of their neighbors layer by layer. However, thehigh computational and memory cost of GCNs due to the recursive neighborhoodexpansion across GCN layers makes it infeasible for training on large graphs.To tackle this issue, several sampling methods during the process ofinformation aggregation have been proposed to train GCNs in a mini-batchStochastic Gradient Descent (SGD) manner. Nevertheless, these samplingstrategies 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 useaggregators with different granularities to gather neighborhood information indifferent layers. Then, a degree-based sampling strategy, which avoids theexponential 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, trainingspeed, convergence speed, and memory cost through a comprehensive set ofexperiments on four commonly used benchmark datasets and a new Ethereumdataset.

MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks