Code for Automated Unsupervised Graph Representation Learning in TKDE'21.
python main.py --emb ./emb/wiki_deepwalk.embedding --adj ./emb/wikipedia.ungraph --saved-path ./out/wiki_spectral.embedding
python main.py --emb ./emb/cora_dgi.embedding --dataset cora --concat-search --prop-types sc ppr heat gaussian
--emb
: path of input embedding--adj
: path of edgelist format adjacency matrix.--concat-search
: search the filters used to concat, default isTrue
.--prop-types
: types of filters to use, options :['ppr', 'heat', 'gaussian', 'sc']
.--max-evals
: num of iterations of automl to optimize loss, default: 100.--loss
: loss function used in AutoML searching, default: 'infomax'--no-eval
: if set, do not evalute the embedding after propagation.--workers
: the number of working threads in AutoML. default: 10. Try to set --workers to a larger number for faster training.--dataset
: (optional).--saved-path
: path to save embeddings.
Currently using optuna
as AutoML tool.
The datasets used in the paper could be downloaded from this link.