Self-supervised Guided Hypergraph Feature Propagation for Semi-supervised Classification with Missing Node Features

by   Chengxiang Lei, et al.

Graph neural networks (GNNs) with missing node features have recently received increasing interest. Such missing node features seriously hurt the performance of the existing GNNs. Some recent methods have been proposed to reconstruct the missing node features by the information propagation among nodes with known and unknown attributes. Although these methods have achieved superior performance, how to exactly exploit the complex data correlations among nodes to reconstruct missing node features is still a great challenge. To solve the above problem, we propose a self-supervised guided hypergraph feature propagation (SGHFP). Specifically, the feature hypergraph is first generated according to the node features with missing information. And then, the reconstructed node features produced by the previous iteration are fed to a two-layer GNNs to construct a pseudo-label hypergraph. Before each iteration, the constructed feature hypergraph and pseudo-label hypergraph are fused effectively, which can better preserve the higher-order data correlations among nodes. After then, we apply the fused hypergraph to the feature propagation for reconstructing missing features. Finally, the reconstructed node features by multi-iteration optimization are applied to the downstream semi-supervised classification task. Extensive experiments demonstrate that the proposed SGHFP outperforms the existing semi-supervised classification with missing node feature methods.


page 1

page 2

page 3

page 4


Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing

In recent years, graph neural networks (GNN) have achieved significant d...

Informative Pseudo-Labeling for Graph Neural Networks with Few Labels

Graph Neural Networks (GNNs) have achieved state-of-the-art results for ...

Multi-Task Hypergraphs for Semi-supervised Learning using Earth Observations

There are many ways of interpreting the world and they are highly interd...

Confidence-Based Feature Imputation for Graphs with Partially Known Features

This paper investigates a missing feature imputation problem for graph l...

Self-supervised Hypergraphs for Learning Multiple World Interpretations

We present a method for learning multiple scene representations given a ...

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...

Learning Hypergraph Labeling for Feature Matching

This study poses the feature correspondence problem as a hypergraph node...

Please sign up or login with your details

Forgot password? Click here to reset