Hello, Louis.
Thanks for providing the code of pytorch version.
I've run the code and try to estimate the performance. I used 40000 graphs(all graphs are finished) and run for 5 epochs. (to speed up the procedure, I changed the method of adjacent matrix), and using 1000 graphs for test.
However, this code only have a performance of about 1.5 (in regards to the worst case) and an average performance of about 1.15 , lower than the ones in the origin code (worst case ~1.1 and avg. case ~1.001). I tried many approaches to improve but failed. Would you like to analyze the reasons? Thanks.
Hello, Louis. Thanks for providing the code of pytorch version. I've run the code and try to estimate the performance. I used 40000 graphs(all graphs are finished) and run for 5 epochs. (to speed up the procedure, I changed the method of adjacent matrix), and using 1000 graphs for test. However, this code only have a performance of about 1.5 (in regards to the worst case) and an average performance of about 1.15 , lower than the ones in the origin code (worst case ~1.1 and avg. case ~1.001). I tried many approaches to improve but failed. Would you like to analyze the reasons? Thanks.