GkGNN extends GNN message passing from neighborhoods to covers via category theory, with the Sieve Neural Networks instantiation achieving zero failures on SRG, CSL, and BREC isomorphism benchmarks.
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A sur- vey on oversmoothing in graph neural networks
21 Pith papers cite this work. Polarity classification is still indexing.
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CAMERA is an ego-decoupled mixture-of-experts model with context-informed gating and one-class objectives for unsupervised fraud detection in text-attributed graphs facing semantic camouflage.
RAM augments relational graph models with attribute-semantic retrieval via random-walk documents and two contrastive augmentations (ATRA, ETRA) to achieve state-of-the-art results on five real-world databases.
MbaGCN combines message aggregation, selective state space transitions, and node state prediction to create a more scalable deep graph convolutional network.
Deep ensembles fail to capture meaningful epistemic uncertainty in message-passing GNNs due to epistemic collapse where independently trained networks converge to similar predictions.
BrainDyn is a sheaf neural ODE model that encodes brain region activity history via LSTMs, projects states through restriction maps, and uses a sheaf Laplacian for message passing to generate continuous-time dynamics on brain graphs.
Neural point-forms are introduced as permutation-invariant neural layers that output learned form-comparison matrices for point clouds, with a claimed consistency proof under sampling and manifold assumptions and competitive results on synthetic and biological data.
SACHI enriches agent representations via graph transformer convolutions over inter-agent graphs to enable holistic information integration, outperforming baselines across five cooperative tasks with statistical significance.
NSPOD is a multigrid-like preconditioner using DeepONet-learned POD subspaces that dramatically cuts Krylov solver iterations for solid mechanics PDEs on unstructured CAD geometries, outperforming algebraic multigrid.
SigGate-GT applies sigmoid gates to attention outputs in graph transformers to reduce over-smoothing, matching prior best on ZINC and setting new SOTA on ogbg-molhiv with gains over GraphGPS.
HISTOGRAPH applies unified layer-wise attention followed by node-wise attention over historical GNN activations to improve graph classification, especially in deep models.
FiLM conditioning targeted at early message-passing layers lets pretrained GNS models generalize to new material properties using only 12 trajectories, a 5-fold data reduction versus multi-task baselines.
A Multi-L KG and Quest-GNN with question-adaptive intra/inter-level message passing and synthesized pre-training data improves multi-hop RAG performance up to 33.8% on high-hop questions.
S³GNN mitigates oversquashing in message-passing networks via lightweight global mixing without strong prior assumptions, yielding up to 10x error reduction and 50% fewer parameters across multiple domains.
QpiGNN provides a quantile-free dual-head architecture for GNN uncertainty quantification that directly optimizes coverage and interval width, yielding 22% higher coverage and 50% narrower intervals than baselines on 19 benchmarks with asymptotic coverage guarantees under mild assumptions.
Measured-only STGNNs (RGATv2, RGSAGE) achieve up to 11 F1 points higher and 6x faster training than RNN baselines for fault location on the IEEE 123-bus feeder under partial observability.
A GCN-GAE model learns node embeddings from directed weighted microservice graphs to flag anomalies via cosine similarity between load-test and live-event representations, with a synthetic injection framework reporting 96% precision.
xAI-Drop introduces an explainability-based topological dropping regularizer for GNNs that outperforms state-of-the-art dropping methods in accuracy and explanation quality on real-world datasets.
A survey reviewing graph rewiring methods that modify topology to mitigate over-squashing and over-smoothing in GNNs.
A survey compiling graph rewiring techniques for mitigating over-squashing and over-smoothing in GNNs.
citing papers explorer
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Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs
GkGNN extends GNN message passing from neighborhoods to covers via category theory, with the Sieve Neural Networks instantiation achieving zero failures on SRG, CSL, and BREC isomorphism benchmarks.
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CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection
CAMERA is an ego-decoupled mixture-of-experts model with context-informed gating and one-class objectives for unsupervised fraud detection in text-attributed graphs facing semantic camouflage.
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From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics
RAM augments relational graph models with attribute-semantic retrieval via random-walk documents and two contrastive augmentations (ATRA, ETRA) to achieve state-of-the-art results on five real-world databases.
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Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
MbaGCN combines message aggregation, selective state space transitions, and node state prediction to create a more scalable deep graph convolutional network.
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Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?
Deep ensembles fail to capture meaningful epistemic uncertainty in message-passing GNNs due to epistemic collapse where independently trained networks converge to similar predictions.
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BrainDyn: A Sheaf Neural ODE for Generative Brain Dynamics
BrainDyn is a sheaf neural ODE model that encodes brain region activity history via LSTMs, projects states through restriction maps, and uses a sheaf Laplacian for message passing to generate continuous-time dynamics on brain graphs.
-
Neural Point-Forms
Neural point-forms are introduced as permutation-invariant neural layers that output learned form-comparison matrices for point clouds, with a claimed consistency proof under sampling and manifold assumptions and competitive results on synthetic and biological data.
-
SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning
SACHI enriches agent representations via graph transformer convolutions over inter-agent graphs to enable holistic information integration, outperforming baselines across five cooperative tasks with statistical significance.
-
NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
NSPOD is a multigrid-like preconditioner using DeepONet-learned POD subspaces that dramatically cuts Krylov solver iterations for solid mechanics PDEs on unstructured CAD geometries, outperforming algebraic multigrid.
-
SigGate-GT: Taming Over-Smoothing in Graph Transformers via Sigmoid-Gated Attention
SigGate-GT applies sigmoid gates to attention outputs in graph transformers to reduce over-smoothing, matching prior best on ZINC and setting new SOTA on ogbg-molhiv with gains over GraphGPS.
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Learning from Historical Activations in Graph Neural Networks
HISTOGRAPH applies unified layer-wise attention followed by node-wise attention over historical GNN activations to improve graph classification, especially in deep models.
-
Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators
FiLM conditioning targeted at early message-passing layers lets pretrained GNS models generalize to new material properties using only 12 trajectories, a 5-fold data reduction versus multi-task baselines.
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Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation
A Multi-L KG and Quest-GNN with question-adaptive intra/inter-level message passing and synthesized pre-training data improves multi-hop RAG performance up to 33.8% on high-hop questions.
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S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
S³GNN mitigates oversquashing in message-passing networks via lightweight global mixing without strong prior assumptions, yielding up to 10x error reduction and 50% fewer parameters across multiple domains.
-
Quantile-Free Uncertainty Quantification in Graph Neural Networks
QpiGNN provides a quantile-free dual-head architecture for GNN uncertainty quantification that directly optimizes coverage and interval width, yielding 22% higher coverage and 50% narrower intervals than baselines on 19 benchmarks with asymptotic coverage guarantees under mild assumptions.
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Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Grids
Measured-only STGNNs (RGATv2, RGSAGE) achieve up to 11 F1 points higher and 6x faster training than RNN baselines for fault location on the IEEE 123-bus feeder under partial observability.
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From Load Tests to Live Streams: Graph Embedding-Based Anomaly Detection in Microservice Architectures
A GCN-GAE model learns node embeddings from directed weighted microservice graphs to flag anomalies via cosine similarity between load-test and live-event representations, with a synthetic injection framework reporting 96% precision.
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xAI-Drop: Don't Use What You Cannot Explain
xAI-Drop introduces an explainability-based topological dropping regularizer for GNNs that outperforms state-of-the-art dropping methods in accuracy and explanation quality on real-world datasets.
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Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
A survey reviewing graph rewiring methods that modify topology to mitigate over-squashing and over-smoothing in GNNs.
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Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
A survey compiling graph rewiring techniques for mitigating over-squashing and over-smoothing in GNNs.
- Self-supervised Adversarial Purification for Graph Neural Networks