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Node Property Prediction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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보존된 평가 기록

235

코드

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

https://github.com/ksadowski13/PCAPass

공식 코드

Dimensionality Reduction Meets Message Passing for Graph Node Embeddings

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/nanw16/GTNet

공식 코드

GTNet: A Tree-Based Deep Graph Learning Architecture

https://github.com/nathanleroux-git/onlinetransformerwithspikingneurons

공식 코드

Online Transformers with Spiking Neurons for Fast Prosthetic Hand Control

https://github.com/pku-dair/gamlp

공식 코드

Graph Attention Multi-Layer Perceptron

https://github.com/pku-ml/contranorm

공식 코드

ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond

https://github.com/ptarau/stanzagraphs

공식 코드

A Gaze into the Internal Logic of Graph Neural Networks, with Logic

https://github.com/pyg-team/pytorch_geometric

공식 코드

Do We Need Anisotropic Graph Neural Networks?

https://github.com/qitianwu/sgformer

공식 코드

SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations

https://github.com/rangan2510/MultiPathGCN

공식 코드

Multipath Graph Convolutional Neural Networks

https://github.com/shenyanghuang/tgb

공식 코드

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

https://github.com/shyam196/egc

공식 코드

Do We Need Anisotropic Graph Neural Networks?

https://github.com/skepsun/SAGN_with_SLE

공식 코드

Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training

https://github.com/snap-research/graphless-neural-networks

공식 코드

Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation

https://github.com/snap-stanford/ogb

공식 코드

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/sunilkmaurya/FSGNN

공식 코드

Improving Graph Neural Networks with Simple Architecture Design

https://github.com/tech-srl/how_attentive_are_gats

공식 코드

How Attentive are Graph Attention Networks?

https://github.com/thudm/crgnn

공식 코드

SCR: Training Graph Neural Networks with Consistency Regularization

https://github.com/thudm/scr

공식 코드

SCR: Training Graph Neural Networks with Consistency Regularization

https://github.com/tkipf/pygcn

공식 코드

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/tkipf/relational-gcn

공식 코드

Modeling Relational Data with Graph Convolutional Networks

https://github.com/tuanh23/e2eg

공식 코드

E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes

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

공식 코드

SIGN: Scalable Inception Graph Neural Networks

https://github.com/vermouthdky/simteg

공식 코드

SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning

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

공식 코드

A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking

https://github.com/weiliu89/caffe

공식 코드

SSD: Single Shot MultiBox Detector

https://github.com/williamleif/GraphSAGE

공식 코드

Inductive Representation Learning on Large Graphs

https://github.com/xavierzw/ogb-geniepath-bs

공식 코드

Bandit Samplers for Training Graph Neural Networks

https://github.com/xiaoxinhe/tape_arxiv_2023

공식 코드

Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

https://github.com/yiminghh/higcn

공식 코드

Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes

https://github.com/yongchao-liu/gipa

공식 코드

GIPA: General Information Propagation Algorithm for Graph Learning

https://github.com/ytchx1999/PyG-OGB-Tricks

공식 코드

Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks

https://github.com/yule-BUAA/HGConv

공식 코드

Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning

https://github.com/yule-BUAA/LEGNN

공식 코드

Label-Enhanced Graph Neural Network for Semi-supervised Node Classification

https://github.com/yule-BUAA/R-HGNN

공식 코드

Heterogeneous Graph Representation Learning with Relation Awareness

https://gitlab.com/cian.unibas.ch/position-regression

공식 코드

Position Regression for Unsupervised Anomaly Detection

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