  Graph neural networks (GNNs) have been popularly used in analyzing
graph-structured data, showing promising results in various applications such
as node classification, link prediction and network recommendation. In this
paper, we present a new graph attention neural network, namely GIPA, for
attributed graph data learning. GIPA consists of three key components:
attention, feature propagation and aggregation. Specifically, the attention
component introduces a new multi-layer perceptron based multi-head to generate
better non-linear feature mapping and representation than conventional
implementations such as dot-product. The propagation component considers not
only node features but also edge features, which differs from existing GNNs
that merely consider node features. The aggregation component uses a residual
connection to generate the final embedding. We evaluate the performance of GIPA
using the Open Graph Benchmark proteins (ogbn-proteins for short) dataset. The
experimental results reveal that GIPA can beat the state-of-the-art models in
terms of prediction accuracy, e.g., GIPA achieves an average test ROC-AUC of
$0.8700\pm 0.0010$ and outperforms all the previous methods listed in the
ogbn-proteins leaderboard.
