Graph Sampling

24 papers with code • 0 benchmarks • 3 datasets

Training GNNs or generating graph embeddings requires graph samples.

Libraries

Use these libraries to find Graph Sampling models and implementations

Most implemented papers

GraphSAINT: Graph Sampling Based Inductive Learning Method

GraphSAINT/GraphSAINT ICLR 2020

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

Heterogeneous Graph Transformer

acbull/pyHGT 3 Mar 2020

Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.

Training Graph Neural Networks with 1000 Layers

lightaime/deep_gcns_torch 14 Jun 2021

Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges.

SIGN: Scalable Inception Graph Neural Networks

twitter-research/sign 23 Apr 2020

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.

Accurate, Efficient and Scalable Graph Embedding

GraphSAINT/GraphSAINT 28 Oct 2018

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

GraphSAINT/GraphSAINT 5 Oct 2020

For feature propagation within subgraphs, we improve cache utilization and reduce DRAM traffic by data partitioning.

Sampling From Large Graphs

benedekrozemberczki/littleballoffur KDD 2006

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 27 Jun 2018

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

kage08/graph_sample_rl 8 Jul 2019

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

StephanieWyt/NMN ACL 2020

This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge.