  Graph Neural Networks (GNNs) have shown success in learning from graph
structured data containing node/edge feature information, with application to
social networks, recommendation, fraud detection and knowledge graph reasoning.
In this regard, various strategies have been proposed in the past to improve
the expressiveness of GNNs. For example, one straightforward option is to
simply increase the parameter size by either expanding the hid-den dimension or
increasing the number of GNN layers. However, wider hidden layers can easily
lead to overfitting, and incrementally adding more GNN layers can potentially
result in over-smoothing.In this paper, we present a model-agnostic
methodology, namely Network In Graph Neural Network (NGNN ), that allows
arbitrary GNN models to increase their model capacity by making the model
deeper. However, instead of adding or widening GNN layers, NGNN deepens a GNN
model by inserting non-linear feedforward neural network layer(s) within each
GNN layer. An analysis of NGNN as applied to a GraphSage base GNN on
ogbn-products data demonstrate that it can keep the model stable against either
node feature or graph structure perturbations. Furthermore, wide-ranging
evaluation results on both node classification and link prediction tasks show
that NGNN works reliably across diverse GNN architectures.For instance, it
improves the test accuracy of GraphSage on the ogbn-products by 1.6% and
improves the hits@100 score of SEAL on ogbl-ppa by 7.08% and the hits@20 score
of GraphSage+Edge-Attr on ogbl-ppi by 6.22%. And at the time of this
submission, it achieved two first places on the OGB link prediction
leaderboard.
