Could you please suggest the configuration settings to get close to the paper results?
For example dominant with inj_cora Table 3 in paper "BOND: Benchmarking..." shows a 82.7 ROC-AUC but I do not seem to be able to go over around 76% which is also the accuracy that shows in the notebook example.
Also dominant with the enron dataset shows auc 57% using the benchmark directory while paper reports 73.1%.
benchmark$ python main.py --model dominant --dataset enron
enron DOMINANT AUC: 0.5715±nan (0.5715) AP: 0.0009±nan (0.0009) Recall: 0.0000±nan (0.0000)
What could I be doing wrong ? This seems to be too low for randomness effects.
A short table with the right settings would be useful.
Thanks,
Could you please suggest the configuration settings to get close to the paper results?
For example dominant with inj_cora Table 3 in paper "BOND: Benchmarking..." shows a 82.7 ROC-AUC but I do not seem to be able to go over around 76% which is also the accuracy that shows in the notebook example.
Also dominant with the enron dataset shows auc 57% using the benchmark directory while paper reports 73.1%.
benchmark$ python main.py --model dominant --dataset enron
enron DOMINANT AUC: 0.5715±nan (0.5715) AP: 0.0009±nan (0.0009) Recall: 0.0000±nan (0.0000)
What could I be doing wrong ? This seems to be too low for randomness effects.
A short table with the right settings would be useful.
Thanks,