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.
arXiv preprint arXiv:2303.10993 , year=
30 Pith papers cite this work, alongside 82 external citations. Polarity classification is still indexing.
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82
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representative citing papers
AGDN is a new GNN framework using a MixScore matrix and anisotropic graph diffusion to outperform prior methods on TSP instances across sizes and distributions.
MAVN adaptively selects and connects virtual nodes in MPNNs via learned dual-perspective preferences, proves it can realize any connectivity pattern, and reports up to 46.5% gains over backbones on nine datasets.
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.
Proves high-probability exact recovery in binary CSBM after O(log n) steps of corrected normalized propagation in dense polylog regime under graph-signal and feature-SNR conditions.
Self-attention mechanisms are used to build mesh-preserving neural surrogates that approximate PFEM dynamics for free-surface flows, delivering accurate transient predictions and improved scalability on 2D and 3D benchmarks.
Early-exit GNNs for link prediction move the speed-quality Pareto frontier on the HeaRT benchmark by allowing implicit early exiting without auxiliary losses.
TSNN equips temporal graphs with per-node time-varying orthogonal frames, explicit transport, and a geometric-residual decoder, delivering competitive or superior link prediction on benchmarks plus theoretical guarantees on sheaf diffusion.
SaFeAU augments collaborative filtering with semantic factor disentanglement and matching to reduce false negatives and capture higher-order signals via matrix factorization.
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.
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.
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.
Boundary-focused contrastive "gravity" loss on selected boundary nodes improves GNN node classification by about one point over an equal-architecture baseline, but the claimed proofs do not cover the implemented loss.
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.
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.
-
AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network
AGDN is a new GNN framework using a MixScore matrix and anisotropic graph diffusion to outperform prior methods on TSP instances across sizes and distributions.
-
Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs
MAVN adaptively selects and connects virtual nodes in MPNNs via learned dual-perspective preferences, proves it can realize any connectivity pattern, and reports up to 46.5% gains over backbones on nine datasets.
-
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.
-
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.
-
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.
-
A Spectral Theory of Normalized Corrected GNN Propagation
Proves high-probability exact recovery in binary CSBM after O(log n) steps of corrected normalized propagation in dense polylog regime under graph-signal and feature-SNR conditions.
-
Attention mechanism for scalable mesh-based neural surrogates of free-surface fluids
Self-attention mechanisms are used to build mesh-preserving neural surrogates that approximate PFEM dynamics for free-surface flows, delivering accurate transient predictions and improved scalability on 2D and 3D benchmarks.
-
Early-Exit Graph Neural Networks for Link Prediction
Early-exit GNNs for link prediction move the speed-quality Pareto frontier on the HeaRT benchmark by allowing implicit early exiting without auxiliary losses.
-
Temporal Sheaf Neural Networks with Dynamic Orthogonal Transport
TSNN equips temporal graphs with per-node time-varying orthogonal frames, explicit transport, and a geometric-residual decoder, delivering competitive or superior link prediction on benchmarks plus theoretical guarantees on sheaf diffusion.
-
Beyond Instance-Level Alignment and Uniformity: Semantic Factor Learning for Collaborative Filtering
SaFeAU augments collaborative filtering with semantic factor disentanglement and matching to reduce false negatives and capture higher-order signals via matrix factorization.
-
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.
-
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.
-
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.
-
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.
-
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.
-
Boundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement
Boundary-focused contrastive "gravity" loss on selected boundary nodes improves GNN node classification by about one point over an equal-architecture baseline, but the claimed proofs do not cover the implemented loss.
-
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.
-
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.
-
Capacity-Controlled Global Attention for Graph Transformers
SigGate-GT adds a per-head sigmoid gate to graph transformer attention outputs, relaxing the softmax convex-combination constraint to reduce over-smoothing and improve stability at ~1% parameter overhead.
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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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Ramanujan Graph Rewiring with Non Negative Resistance Curvature
Introduces Ramanujan Propagation as a graph rewiring method for GNNs that leverages Ramanujan graphs to ensure non-negative resistance curvature while preserving local connectivity and outperforming prior rewiring techniques.
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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.