  Message passing neural networks (MPNNs) learn the representation of
graph-structured data based on graph original information, including node
features and graph structures, and have shown astonishing improvement in node
classification tasks. However, the expressive power of MPNNs is upper bounded
by the first-order Weisfeiler-Leman test and its accuracy still has room for
improvement. This work studies how to improve MPNNs' expressiveness and
generalizability by fully exploiting graph original information both
theoretically and empirically. It further proposes a new GNN model called INGNN
(INformation-enhanced Graph Neural Network) that leverages the insights to
improve node classification performance. Extensive experiments on both
synthetic and real datasets demonstrate the superiority (average rank 1.78) of
our INGNN compared with state-of-the-art methods.
