Graph Sampling
41 papers with code • 0 benchmarks • 3 datasets
Training GNNs or generating graph embeddings requires graph samples.
Benchmarks
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Libraries
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Friendster
LastFM Asia
Most implemented papers
GraphSAINT: Graph Sampling Based Inductive Learning Method
GraphSAINT/GraphSAINT
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ICLR 2020
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.
Heterogeneous Graph Transformer
acbull/pyHGT
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Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.
SIGN: Scalable Inception Graph Neural Networks
Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media.
Training Graph Neural Networks with 1000 Layers
Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges.
Accurate, Efficient and Scalable Graph Embedding
However, a major challenge is to reduce the complexity of layered GCNs and make them parallelizable and scalable on very large graphs -- state-of the art techniques are unable to achieve scalability without losing accuracy and efficiency.
Accurate, Efficient and Scalable Training of Graph Neural Networks
For feature propagation within subgraphs, we improve cache utilization and reduce DRAM traffic by data partitioning.
Sampling From Large Graphs
Thus graph sampling is essential. The natural questions to ask are (a) which sampling method to use, (b) how small can the sample size be, and (c) how to scale up the measurements of the sample (e. g., the diameter), to get estimates for the large graph.
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
wooden-spoon/relational-ERM
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We solve this problem using recent ideas from graph sampling theory to (i) define an empirical risk for relational data and (ii) obtain stochastic gradients for this empirical risk that are automatically unbiased.
Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network.
Neighborhood Matching Network for Entity Alignment
This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge.
