Hierarchical Representation Learning for Attributed Networks

This repository provides a reference implementation of HANE as described in the [paper](DOI: 10.1109/TKDE.2021.3117274).

Required Packages

• tensorflow
• numpy
• scipy
• scikit-learn
• networkx
• gensim (only for using DeepWalk as base embedding method)
• theano (only for using NetMF as base embedding method)

dataset

baiduyunpan：https://pan.baidu.com/s/1dD6TpleAUVq7AKk-lev1pw (fnpe)

How To Run

Use python main.py to run the code with all the default settings. Here are some useful arguments that can be passed to the program:

• --data: name of the dataset (file located in ./dataset), e.g., --data /cora.
• --basic-embed: name of the base embedding method, e.g., --basic-embed deepwalk.
• --coarsen-level: number of levels for coarsening, e.g., --coarsen-level 2.
• --embed-dim: dimensionality for embedding, e.g., --embed-dim 128.
• --no-eval: will not evaluate the embeddings if set (.truth file will not be required then).
• --workers: number of processes to run the code.
• --refine-type: refinement method, including MD-gcn , MD-dumb (without model training), and MD-gs (using GraphSAGE).

If you find HANE useful in your research, please cite our paper:

@ S. Zhao, Z. Du, J. Chen, Y. Zhang, J. Tang and P. Yu, "Hierarchical Representation Learning for Attributed Networks," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2021.3117274.