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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 91–100 of 101 papers

TitleStatusHype
AGS-GNN: Attribute-guided Sampling for Graph Neural NetworksCode0
Joint Network Topology Inference via a Shared Graphon ModelCode0
A Topology-aware Analysis of Graph Collaborative FilteringCode0
Learning Dynamic Preference Structure Embedding From Temporal NetworksCode0
Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying GlassCode0
A Sampling-based Framework for Hypothesis Testing on Large Attributed GraphsCode0
Influence maximization in unknown social networks: Learning Policies for Effective Graph SamplingCode0
Accurate, Efficient and Scalable Graph EmbeddingCode0
Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and RobustnessCode0
Pre-Training on Dynamic Graph Neural NetworksCode0
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