Adversarial Deep Network Embedding for Cross-Network Node Classification (ACDNE)

This repository contains the author's implementation in Tensorflow for the paper "Adversarial Deep Network Embedding for Cross-Network Node Classifications".

Environment Requirement

The code has been tested running under Python 3.6.2. The required packages are as follows:

• python == 3.6.2

• tensorflow == 1.13.1

• numpy == 1.16.2

• scipy == 1.2.1

• sklearn == 0.21.1

Datasets

input/ contains the 5 datasets used in our paper.

Each ".mat" file stores a network dataset, where

the variable "network" represents an adjacency matrix,

the variable "attrb" represents a node attribute matrix,

the variable "group" represents a node label matrix.

Code

"ACDNE_model.py" is the implementation of the ACDNE model.

"ACDNE_test_Blog.py" is an example case of the cross-network node classification task from Blog1 to Blog2 networks.

"ACDNE_test_citation.py" is an example case of the cross-network node classification task from citationv1 to dblpv7 networks.

Plese cite our paper as:

Xiao Shen, Quanyu Dai, Fu-lai Chung, Wei Lu, and Kup-Sze Choi. Adversarial Deep Network Embedding for Cross-Network Node Classification. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), pages 2991-2999, 2020.

Pytorch Implementation of ACDNE can be found at:

https://github.com/3480430977/ACDNE