Table 2 Summary of Graph Convolution Network with the technique used, datasets used, and performance measure (accuracy)

[33]

(2016)

Node Classification

Graph Convolution Network

CORA

GCN model with 2 layers

ReLU function

82.98

[33]

(2017)

Semi-Supervised Node Classification

Graph Convolution Network

Zachary's Karate Club

GCN model with 2 layers

ReLU function

90%

[33]

(2016)

Semi-Supervised Node Classification

Graph Convolution Network

CORA

GCN model with 2 layers

ReLU function

81.5%

[39]

(2019)

Text Classification

GCN for Text Classification

20NG

Ohsumed

R52

R8

MR

2 Layer GCN

ReLU function

0.8634 ± 0.0009 0.9707 ± 0.0010 0.9356 ± 0.0018 0.6836 ± 0.0056 0.7674 ± 0.0020

[31]

(2019)

Node Classification

Node Classification

Cora,

Citeseer,

Pubmed

Reddit

4-layer GCN

ReLU function

74.60%

61.40%

86.20%

50.51%

[40]

(2018)

Quiz

Question Answering by Reasoning

WIKIHOP

Two layers MLP

65.3% to 68.7%

[41]

(2018)

Node Classification

Node Classification

Cora,

Citeseer, Pubmed

2 Layer GCN

ReLU function

70.3%

81.5%

79.0%

[42]

(2019)

Node Classification

Hierarchical graph convolutional networks for semi-supervised node classification

Cora,

Citeseer,

Pubmed

NELL

2 Layer GCN

ReLU function

70.3%

81.5%

79.0%

73.0%

[43]

(2020)

Traffic prediction

Traffic prediction

Real-time dataset

Message passing technique + Graph Convolution Network

70–75%

[44]

(2023)

Motion Capture for Sporting Events

Graph Convolutional Neural Networks and Single Target Pose Estimation

COCO dataset

Graph Neural Network Combined With High Resolution Network (HRNET)

79.3

[45]

(2023)

Defect Recognition

Deep Graph Convolutional Neural Network

9 different dataset

Graph Convolutional Neural Network (GCNN)

Around 90%

[46]

(2023)

Flow Prediction

Graph Convolutional Long Short-Term Memory Neural Network Model

Société de transport de Laval (STL)

MLP,

CNN,

LSTM,

BNG-ConvLSTM = bus network graph convolutional long short-term memory

73.3

70.0

80.2

85.3