GIPA: General Information Propagation Algorithm for Graph Learning

by   Qinkai Zheng, et al.

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 ROC-AUC of 0.8700± 0.0010 and outperforms all the previous methods listed in the ogbn-proteins leaderboard.


page 1

page 2

page 3

page 4


Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction

Graph Neural Networks (GNNs) have been widely applied to various fields ...

On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features

While Graph Neural Networks (GNNs) have recently become the de facto sta...

Active Learning for Graph Neural Networks via Node Feature Propagation

Graph Neural Networks (GNNs) for prediction tasks like node classificati...

Graph Attention Multi-Layer Perceptron

Graph neural networks (GNNs) have recently achieved state-of-the-art per...

Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

3D-related inductive biases like translational invariance and rotational...

Generalizing Aggregation Functions in GNNs:High-Capacity GNNs via Nonlinear Neighborhood Aggregators

Graph neural networks (GNNs) have achieved great success in many graph l...

Feature Propagation on Graph: A New Perspective to Graph Representation Learning

We study feature propagation on graph, an inference process involved in ...