Recently, graph neural networks have been adopted in a wide variety of
applications ranging from relational representations to modeling irregular data
domains such as point clouds and social graphs. However, the space of graph
neural network architectures remains highly fragmented impeding the development
of optimized implementations similar to what is available for convolutional
neural networks. In this work, we present BiGraphNet, a graph neural network
architecture that generalizes many popular graph neural network models and
enables new efficient operations similar to those supported by ConvNets. By
explicitly separating the input and output nodes, BiGraphNet: (i) generalizes
the graph convolution to support new efficient operations such as coarsened
graph convolutions (similar to strided convolution in convnets), multiple input
graphs convolution and graph expansions (unpooling) which can be used to
implement various graph architectures such as graph autoencoders, and graph
residual nets; and (ii) accelerates and scales the computations and memory
requirements in hierarchical networks by performing computations only at
specified output nodes.

本文通过提出一种名为 BiGraphNet 的图神经网络架构，加速和扩展了图像自编码器、图像残差网络等图神经网络的优化实现，进一步拓展了卷积神经网络类的新型高效操作。