Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on large-scale graphs is the scalability issue that it incurs heavy cost both in computation and memory due to the uncontrollable neighborhood expansion across layers. In this paper, we accelerate the training of GCNs through developing an adaptive layer-wise sampling method. By constructing the network layer by layer in a top-down passway, we sample the lower layer conditioned on the top one, where the sampled neighborhoods are shared by different parent nodes and the over expansion is avoided owing to the fixed-size sampling. More importantly, the proposed sampler is adaptive and applicable for explicit variance reduction, which in turn enhances the training of our method. Furthermore, we propose a novel and economical approach to promote the message passing over distant nodes by applying skip connections. Intensive experiments on several benchmarks verify the effectiveness of our method regarding the classification accuracy while enjoying faster convergence speed.

Without careful design, GCNs can fail miserably even with multiple layers and nonlinear activation functions.

Recent work has shown that a much simpler model, simple graph convolution (SGC) (Wu et al., 2019),iscompetitivewithGCNs incommon graph machine learning benchmarks.

Accurately answering aquestionabout agivenimage requires combining observations with general knowledge. While this is effortless for humans, reasoning with general knowledge remains analgorithmic challenge. Toadvance research inthisdirection anovel'fact-based' visual question answering (FVQA) taskhas been introduced recently along with a large set of curated facts which link two entities, i.e., two possible answers, via a relation.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/