Source code for KDD2020 "AM-GCN: Adaptive Multi-channel Graph Convolutional Networks"
- python == 3.7
- Pytorch == 1.1.0
- Numpy == 1.16.2
- SciPy == 1.3.1
- Networkx == 2.4
- scikit-learn == 0.21.3
python main.py -d dataset -l labelrate
- dataset: including [citeseer, uai, acm, BlogCatalog, flickr, coraml], required.
- labelrate: including [20, 40, 60], required.
e.g.
python main.py -d citeseer -l 20
- Citeseer: Semi-Supervised Classifcation with Graph Convolutional Networks.
- UAI2010: A Unifed Weakly Supervised Framework for Community Detection and Semantic Matching.
- ACM: Heterogeneous Graph Attention Network.
- BlogCatalog,Flickr: Co-Embedding Attributed Networks.
- CoraFull: Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking.
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
Recorded in ./AMGCN/config/[L/C][dataset].ini
e.g. ./AMGCN/config/20citeseer.ini
- Model_setup: parameters for training AM-GCN, such as nhid1, nhid2, beta, theta...
- Data_setting: dataset setttings, such as paths for input, node numbers, feature dimensions...
@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}
}