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AM-GCN

Source code for KDD2020 "AM-GCN: Adaptive Multi-channel Graph Convolutional Networks"

Environment Settings

Usage

python main.py -d dataset -l labelrate

e.g.

python main.py -d citeseer -l 20

Data

Link

Usage

Please first unzip the data folders and then use. The files in folders are as follows:

citeseer/
├─citeseer.edge: edge file.  
├─citeseer.feature: feature file.  
├─citeseer.label: label file.  
├─testL/C.txt: test file. L/C, i.e., Label pre Class, L/C = 20, 40, 60.   
├─trainL/C.txt: train file. L/C, i.e., Label pre Class, L/C = 20, 40, 60.  
└─knn
   └─ck.txt: feature graph file. k = 2~9

Parameter Settings

Recorded in ./AMGCN/config/[L/C][dataset].ini
e.g. ./AMGCN/config/20citeseer.ini

Reference

@inproceedings{wang2020gcn,
  title={AM-GCN: Adaptive Multi-channel Graph Convolutional Networks},
  author={Wang, Xiao and Zhu, Meiqi and Bo, Deyu and Cui, Peng and Shi, Chuan and Pei, Jian},
  booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={1243--1253},
  year={2020}
}