GraphNPE recovers a significantly lower central density for Boötes I consistent with a core while Draco remains marginally cuspy, and demonstrates that higher-order velocity moments reduce bias in dynamical modeling.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
abstract
In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Importantly, the proposed technique offers the same linear computational complexity and constant learning complexity as classical CNNs, while being universal to any graph structure. Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs.
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representative citing papers
GravityGraphSAGE adapts GraphSAGE with a gravity-inspired decoder to outperform prior graph deep learning methods on directed link prediction across citation networks and 16 real-world graphs.
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
TED is a heterogeneous GNN that uses related party transaction groups and hierarchical attention to detect tax evasion, claiming significant outperformance over prior methods on two real tax datasets.
Transductive Sharpening adds an entropy-minimization term on unlabeled-node predictions to the training objective for graph node classification.
MixTGFormer reports state-of-the-art 3D pose estimation errors of 37.6 mm on Human3.6M and 15.7 mm on MPI-INF-3DHP by using parallel GCN-Transformer streams with SE layers for local-global feature fusion.
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.
citing papers explorer
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Dark Matter in Draco and Bo\"otes I: Hints of a Core in an Ultra-Faint Dwarf from Simulation-Based Inference
GraphNPE recovers a significantly lower central density for Boötes I consistent with a core while Draco remains marginally cuspy, and demonstrates that higher-order velocity moments reduce bias in dynamical modeling.
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GravityGraphSAGE: Link Prediction in Directed Attributed Graphs
GravityGraphSAGE adapts GraphSAGE with a gravity-inspired decoder to outperform prior graph deep learning methods on directed link prediction across citation networks and 16 real-world graphs.
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph
TED is a heterogeneous GNN that uses related party transaction groups and hierarchical attention to detect tax evasion, claiming significant outperformance over prior methods on two real tax datasets.
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Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
Transductive Sharpening adds an entropy-minimization term on unlabeled-node predictions to the training objective for graph node classification.
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Dual-stream Spatio-Temporal GCN-Transformer Network for 3D Human Pose Estimation
MixTGFormer reports state-of-the-art 3D pose estimation errors of 37.6 mm on Human3.6M and 15.7 mm on MPI-INF-3DHP by using parallel GCN-Transformer streams with SE layers for local-global feature fusion.
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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.
- KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging