Graph filtering with multiple shift matrices
J Fan, C Tepedelenlioglu… - ICASSP 2019-2019 IEEE …, 2019 - ieeexplore.ieee.org
We propose a novel graph filtering method for semi-supervised classification that adopts
multiple graph shift matrices to obtain more flexibility in dealing with misleading features …
multiple graph shift matrices to obtain more flexibility in dealing with misleading features …
Design of affinity-aware encoding by embedding graph centrality for graph classification
W Dong, J Wu, Z Bai, W Li, W Qiao - Neurocomputing, 2020 - Elsevier
Deep learning methods for graph classification are critical for graph data mining. Recently,
graph convolutional networks (GCNs) have been able to achieve state-of-the-art node …
graph convolutional networks (GCNs) have been able to achieve state-of-the-art node …
Evaluation of network architecture and data augmentation methods for deep learning in chemogenomics
B Playe, V Stoven - bioRxiv, 2019 - biorxiv.org
Among virtual screening methods that have been developed to facilitate the drug discovery
process, chemogenomics presents the particularity to tackle the question of predicting …
process, chemogenomics presents the particularity to tackle the question of predicting …
Machine learning approaches for drug virtual screening
B Playe - 2019 - pastel.hal.science
The rational drug discovery process has limited success despite all the advances in
understanding diseases, and technological breakthroughs. Indeed, the process of drug …
understanding diseases, and technological breakthroughs. Indeed, the process of drug …
Characterize and Transfer Attention in Graph Neural Networks
Does attention matter and, if so, when and how? Our study on both inductive and
transductive learning suggests that datasets have a strong influence on the effects of …
transductive learning suggests that datasets have a strong influence on the effects of …