Self-supervised Graph Neural Networks via Diverse and Interactive Message Passing.
- torch 1.7.1
- sklearn 0.21.3
- numpy 1.16.0
- scipy 1.5.4
- munkres 1.1.4
Train and evaluate the node_classify model by executing
python -u execute.py --dataset cora --nb_epochs 2000 --patience 20 --numb_start 4 --numb_end 5 --chu_start 1 --chu_end 2 --chu_strip 0.5 --h1 -0.1 --h2 -0.1 --k_numb1 0 --k_numb2 1
The --dataset argument should be one of [ cora, citeseer, pubmed, amazon_electronics_computers, amazon_electronics_photo, ms_academic_cs, ms_academic_phy ].
Train and evaluate the graph_classify model by executing
python graph_classify/graph_classify.py
The --dataset argument should be one of [ MUTAG, PTC_MR, IMDB-BINARY, IMDB-MULTI, REDDIT-BINARY, REDDIT-MULTI-5K].