Scalable Graph Neural Networks via Bidirectional Propagation
This addresses scalability issues in GNNs for large-scale graph data, offering a practical solution for applications like social networks or recommendation systems, though it appears incremental by building on existing sampling techniques.
The paper tackles the problem of scaling Graph Neural Networks (GNNs) to large graphs with billions of edges, presenting GBP, a method that achieves state-of-the-art performance with significantly reduced training and testing time, notably handling a graph with over 60 million nodes and 1.8 billion edges in under half an hour on a single machine.
Graph Neural Networks (GNN) is an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most existing methods use "graph sampling" or "layer-wise sampling" techniques to reduce training time. However, these methods still suffer from degrading performance and scalability problems when applying to graphs with billions of edges. This paper presents GBP, a scalable GNN that utilizes a localized bidirectional propagation process from both the feature vectors and the training/testing nodes. Theoretical analysis shows that GBP is the first method that achieves sub-linear time complexity for both the precomputation and the training phases. An extensive empirical study demonstrates that GBP achieves state-of-the-art performance with significantly less training/testing time. Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1.8 billion edges in less than half an hour on a single machine. The codes of GBP can be found at https://github.com/chennnM/GBP .