Gaussian Sheaf Neural Networks derive a sheaf Laplacian for Gaussian node features on graphs to preserve their geometric structure during message passing.
International Conference on Learning Representations , year=
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UNVERDICTED 3representative citing papers
Presents MBFC-2025 dataset and multi-view embeddings with fusion methods for media bias and factuality, reporting SOTA results on ACL-2020 and new benchmarks on MBFC-2025.
GPR-GAE is a novel self-supervised graph auto-encoder using multiple Generalized PageRank filters that serves as a plug-and-play purifier achieving state-of-the-art robustness for GNNs against structural attacks.
citing papers explorer
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Gaussian Sheaf Neural Networks
Gaussian Sheaf Neural Networks derive a sheaf Laplacian for Gaussian node features on graphs to preserve their geometric structure during message passing.
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A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis
Presents MBFC-2025 dataset and multi-view embeddings with fusion methods for media bias and factuality, reporting SOTA results on ACL-2020 and new benchmarks on MBFC-2025.
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Self-supervised Adversarial Purification for Graph Neural Networks
GPR-GAE is a novel self-supervised graph auto-encoder using multiple Generalized PageRank filters that serves as a plug-and-play purifier achieving state-of-the-art robustness for GNNs against structural attacks.