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Graph Sampling

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전체 기법 전체 데이터셋

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Training GNNs or generating graph embeddings requires graph samples.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/CrawlScript/tf_geometric

공식 코드

Efficient Graph Deep Learning in TensorFlow with tf_geometric

https://github.com/GraphSAINT/GraphSAINT

공식 코드

GraphSAINT: Graph Sampling Based Inductive Learning Method

https://github.com/GraphSAINT/GraphSAINT/tree/master/ipdps19_cpp

공식 코드

Accurate, Efficient and Scalable Graph Embedding

https://github.com/Joinn99/FPSR

공식 코드

Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation

https://github.com/Mobzhang/PT-DGNN

공식 코드

Pre-Training on Dynamic Graph Neural Networks

https://github.com/Spider-scnu/TSP

공식 코드

Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances

https://github.com/StephanieWyt/NMN

공식 코드

Neighborhood Matching Network for Entity Alignment

https://github.com/acbull/pyHGT

공식 코드

Heterogeneous Graph Transformer

https://github.com/benedekrozemberczki/littleballoffur

공식 코드

Little Ball of Fur: A Python Library for Graph Sampling

https://github.com/carrieww/graphht

공식 코드

A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs

https://github.com/chennnM/GBP

공식 코드

Scalable Graph Neural Networks via Bidirectional Propagation

https://github.com/cornell-zhang/preprop-gnn

공식 코드

Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs

https://github.com/czt117/csrms

공식 코드

Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning

https://github.com/dfdazac/grapes

공식 코드

GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks

https://github.com/doujiang-zheng/dynamic-preference-structure

공식 코드

Learning Dynamic Preference Structure Embedding From Temporal Networks

https://github.com/graph-0/cdgs

공식 코드

Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation

https://github.com/gt-tdalab/dgl-coop

공식 코드

Cooperative Minibatching in Graph Neural Networks

https://github.com/hicai-zju/gs-meta

공식 코드

Graph Sampling-based Meta-Learning for Molecular Property Prediction

https://github.com/huqiachn/his-gcn

공식 코드

Hierarchical graph sampling based minibatch learning with chain preservation and variance reduction

https://github.com/jeongminpark417/gids

공식 코드

Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses

https://github.com/kage08/graph_sample_rl

공식 코드

Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling

https://github.com/lamm-mit/graphreasoning

공식 코드

Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning

https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb_eff/ogbn_proteins

공식 코드

Training Graph Neural Networks with 1000 Layers

https://github.com/maoxinn/psr

공식 코드

Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness

https://github.com/mn51/jointinf_graphs_graphon

공식 코드

Joint Network Topology Inference via a Shared Graphon Model

https://github.com/mschauer/CausalInference.jl

공식 코드

Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs

https://github.com/qianghuangwhu/tf-tgn

공식 코드

Retrofitting Temporal Graph Neural Networks with Transformer

https://github.com/quiver-team/torch-quiver

공식 코드

Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness

https://github.com/shengcailiao/QAConv

공식 코드

Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification

https://github.com/siddhartha047/AGS-GNN

공식 코드

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

https://github.com/sisinflab/graph-characteristics

공식 코드

A Topology-aware Analysis of Graph Collaborative Filtering

https://github.com/smiles724/MatchExplainer

공식 코드

Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching

https://github.com/tensorflow/gnn

공식 코드

TF-GNN: Graph Neural Networks in TensorFlow

https://github.com/thu-spmi/ST-NAS

공식 코드

Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through Gradients

https://github.com/twitter-research/sign

공식 코드

SIGN: Scalable Inception Graph Neural Networks

https://github.com/vita-group/graph_ladling

공식 코드

Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.