Papers with Code 아카이브

Node Property Prediction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

2

평가표

5

보존된 평가 기록

235

코드

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

https://github.com/chalothon/Graph-Convolutional-Networks-for-Relational-Link-Prediction

Modeling Relational Data with Graph Convolutional Networks

https://github.com/chandra411/Product-Detection

SSD: Single Shot MultiBox Detector

https://github.com/changminwu/expandergnn

Simplifying Graph Convolutional Networks

https://github.com/chaowang1994/caffe-ssd

SSD: Single Shot MultiBox Detector

https://github.com/chatterjeeayan/upna

Inductive Representation Learning on Large Graphs

https://github.com/chenjian123456/caffe-ssd

SSD: Single Shot MultiBox Detector

https://github.com/chenjunweii/mxnet-ssd

SSD: Single Shot MultiBox Detector

https://github.com/chennnM/GCNII/tree/master/PyG/ogbn-arxiv

Simple and Deep Graph Convolutional Networks

https://github.com/chihyanghsu0805/object_detection_yolo

SSD: Single Shot MultiBox Detector

https://github.com/clin366/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/code-implementation1/Code9/tree/main/SSD

SSD: Single Shot MultiBox Detector

https://github.com/cornell-zhang/Polynormer/tree/master/large_graph_exp

Polynormer: Polynomial-Expressive Graph Transformer in Linear Time

https://github.com/cvg/scrstudio

node2vec: Scalable Feature Learning for Networks

https://github.com/cybermonic/cage-4-submission

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/danielegrattarola/keras-gat

Graph Attention Networks

https://github.com/darnbi/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/davidpicard/homm

Graph Attention Networks

https://github.com/deepchem/moleculenet

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/destwang/InterHT

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/dglai/wsdm2022-challenge

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dmeoli/neuro-sat

Graph Attention Networks

https://github.com/dmlc/dgl/blob/master/python/dgl/nn/pytorch/conv/gatv2conv.py

How Attentive are Graph Attention Networks?

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin

Inductive Representation Learning on Large Graphs

https://github.com/dmlc/dgl/tree/master/examples/mxnet/graphsage

Inductive Representation Learning on Large Graphs

https://github.com/dmlc/dgl/tree/master/examples/mxnet/rgcn

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/sgc

Simplifying Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/cluster_gcn

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/correct_and_smooth

Combining Label Propagation and Simple Models Out-performs Graph Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/dagnn

Towards Deeper Graph Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/deepergcn

DeeperGCN: All You Need to Train Deeper GCNs

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/graphsaint

GraphSAINT: Graph Sampling Based Inductive Learning Method

https://github.com/dmlc/dgl/tree/master/examples/pytorch/hgt

Heterogeneous Graph Transformer

https://github.com/dmlc/dgl/tree/master/examples/pytorch/jknet

Representation Learning on Graphs with Jumping Knowledge Networks

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