  Graph neural networks (GNNs) have been demonstrated as a powerful tool for
analysing non-Euclidean graph data. However, the lack of efficient distributed
graph learning systems severely hinders applications of GNNs, especially when
graphs are big, of high density or with highly skewed node degree
distributions. In this paper, we present a new distributed graph learning
system GraphTheta, which supports multiple training strategies and enables
efficient and scalable learning on big graphs. GraphTheta implements both
localized and globalized graph convolutions on graphs, where a new graph
learning abstraction NN-TGAR is designed to bridge the gap between graph
processing and graph learning frameworks. A distributed graph engine is
proposed to conduct the stochastic gradient descent optimization with
hybrid-parallel execution. Moreover, we add support for a new cluster-batched
training strategy in addition to the conventional global-batched and
mini-batched ones. We evaluate GraphTheta using a number of network data with
network size ranging from small-, modest- to large-scale. Experimental results
show that GraphTheta scales almost linearly to 1,024 workers and trains an
in-house developed GNN model within 26 hours on Alipay dataset of 1.4 billion
nodes and 4.1 billion attributed edges. Moreover, GraphTheta also obtains
better prediction results than the state-of-the-art GNN methods. To the best of
our knowledge, this work represents the largest edge-attributed GNN learning
task conducted on a billion-scale network in the literature.
