# Graph Sampling

35 papers with code • 0 benchmarks • 3 datasets

Training GNNs or generating graph embeddings requires graph samples.

## Benchmarks

These leaderboards are used to track progress in Graph Sampling
## Libraries

Use these libraries to find Graph Sampling models and implementations## Most implemented papers

# GraphSAINT: Graph Sampling Based Inductive Learning Method

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.

# Heterogeneous Graph Transformer

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

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.