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Scale invariance in node classification is demonstrated and applied in graph transformation to develop ScaleNet, which achieves state-of-the-art performance on both homophilic and heterophilic directed graphs
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Time Series
Equal...
Equal contributors: A.P. and F.M.; Lead contact: A.P
Accep...
Accepted by NeurIPS 2024
13 pa...
13 pages, 9 figures, 2 tables
28 pa...
28 pages, 20 figures, 5 tables
Trajectory
Accep...
Accepted at the IEEE International Conference on Systems, Man, and Cybernetics 2024
In Vi...
In Vietnamese language, in the 25th National Conference on Electronics, Communications and Information Technology (REV-ECIT 2022), Hanoi, Vietnam
Submi...
Submitted; 42 pages, comments are welcome
arXiv...
arXiv admin note: text overlap with arXiv:2401.13784
Accep...
Accepted to ACCV 2024
Accep...
Accepted at the IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2024
Graph Neural Networks
Scale...
Scale invariance in node classification is demonstrated and applied in graph transformation to develop ScaleNet, which achieves state-of-the-art performance on both homophilic and heterophilic directed graphs
Under...
Under review as a conference paper at ICLR 2025
27 Pa...
27 Pages. Extended journal version of SupplyGraph (arXiv:2401.15299). In Review
We fo...
We found that there were critical problems in our paper, and we needed to redo the experiment, which was incomplete
Accep...
Accepted by Transactions on Machine Learning Research (TMLR)
Accep...
Accepted for BIBM 2024 AIBH Workshop
28 pa...
28 pages, 16 figures, preprint; v2: updated github url
Major...
Major equal contributors: H.F.H., T.B., B.G., L.S.A.M., A.P., A.V., J.W.; Significant equal contributors: J.B., A.B.M., S.C., I.F., B.H., R.J., K.K., V.M., F.M., M.D.M., O.R., H.S., L.X., E.S.Y., A.Z.; Other equal contributors: A.J.B., R.J.B., B.P.B., J.S., S.S; Lead contact: H.F.H
37 pa...
37 pages,25 figures, 10 tables
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