Self-supervised Guided Hypergraph Feature Propagation for
Semi-supervised Classification with Missing Node Features
- URL: http://arxiv.org/abs/2302.08250v1
- Date: Thu, 16 Feb 2023 12:13:46 GMT
- Title: Self-supervised Guided Hypergraph Feature Propagation for
Semi-supervised Classification with Missing Node Features
- Authors: Chengxiang Lei, Sichao Fu, Yuetian Wang, Wenhao Qiu, Yachen Hu, Qinmu
Peng and Xinge You
- Abstract summary: 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.
Then, the reconstructed node features are fed to a two-layer GNNs to construct a pseudo-label hypergraph.
Extensive experiments demonstrate that the proposed SGHFP outperforms the existing semi-supervised classification with missing node feature methods.
- Score: 9.684903457117917
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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