1 citations · 1 across the 1 of their papers we have counts for
3 papers
On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction
Edward Elson Kosasih, Joaquin Cabezas, Xavier Sumba +5
In order to advance large-scale graph machine learning, the Open Graph Benchmark Large Scale Challenge (OGB-LSC) was proposed at the KDD Cup 2021. The PCQM4M-LSC dataset defines a…
AttrE2vec: Unsupervised Attributed Edge Representation Learning
Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla
Representation learning has overcome the often arduous and manual featurization of networks through (unsupervised) feature learning as it results in embeddings that can apply to a…
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz +2
Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on…