For node classification, Graph Neural Networks (GNN) assign predefined labels
to graph nodes according to node features propagated along the graph structure.
Apart from the traditional end-to-end manner inherited from deep learning, many
subsequent works input assigned labels into GNNs to improve their
classification performance. Such label-inputted GNNs (LGNN) combine the
advantages of learnable feature propagation and long-range label propagation,
producing state-of-the-art performance on various benchmarks. However, the
theoretical foundations of LGNNs are not well-established, and the combination
is with seam because the long-range propagation is memory-consuming for
optimization. To this end, this work interprets LGNNs with the theory of
Implicit GNN (IGNN), which outputs a fixed state point of iterating its network
infinite times and optimizes the infinite-range propagation with constant
memory consumption. Besides, previous contributions to LGNNs inspire us to
overcome the heavy computation in training IGNN by iterating the network only
once but starting from historical states, which are randomly masked in
forward-pass to implicitly guarantee the existence and uniqueness of the fixed
point. Our improvements to IGNNs are network agnostic: for the first time, they
are extended with complex networks and applied to large-scale graphs.
Experiments on two synthetic and six real-world datasets verify the advantages
of our method in terms of long-range dependencies capturing, label transitions
modelling, accuracy, scalability, efficiency, and well-posedness.

本文介绍了一种名为 Implicit GNN 的理论框架，能够通过只迭代网络一次但起始状态随机遮蔽的方式来优化无限传播的长距离标签，并将这种改进扩展到了复杂网络和大规模图上，实验结果证明其性能优于其他方法。