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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 51–75 of 101 papers

TitleStatusHype
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment—0
Node Copying: A Random Graph Model for Effective Graph Sampling—0
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable RecommendationCode0
TF-GNN: Graph Neural Networks in TensorFlowCode3
Neuro-Symbolic Procedural Planning with Commonsense Prompting—0
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks—0
HyFactor: Hydrogen-count labelled graph-based defactorization AutoencoderCode1
Learning Large-scale Network Embedding from Representative Subgraph—0
Learning Dynamic Preference Structure Embedding From Temporal NetworksCode0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
Gophormer: Ego-Graph Transformer for Node Classification—0
Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment—0
Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery—0
Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and RobustnessCode0
Training Graph Neural Networks with 1000 LayersCode2
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG—0
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-IdentificationCode0
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment—0
Pre-Training on Dynamic Graph Neural NetworksCode0
GIST: Distributed Training for Large-Scale Graph Convolutional NetworksCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
Generalize a Small Pre-trained Model to Arbitrarily Large TSP InstancesCode1
Learning Parameters for Balanced Index Influence Maximization—0
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through GradientsCode1
Text Graph Transformer for Document Classification—0
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