Convolutional neural networks on graphs with chebyshev approximation, revisited
Designing spectral convolutional networks is a challenging problem in graph learning.
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …
iGRLDTI: an improved graph representation learning method for predicting drug–target interactions over heterogeneous biological information network
Motivation The task of predicting drug–target interactions (DTIs) plays a significant role in
facilitating the development of novel drug discovery. Compared with laboratory-based …
facilitating the development of novel drug discovery. Compared with laboratory-based …
Graph attention multi-layer perceptron
Graph neural networks (GNNs) have achieved great success in many graph-based
applications. However, the enormous size and high sparsity level of graphs hinder their …
applications. However, the enormous size and high sparsity level of graphs hinder their …
Graph neural networks with learnable and optimal polynomial bases
Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined
polynomial basis and learn the coefficients from the training data. It has been observed that …
polynomial basis and learn the coefficients from the training data. It has been observed that …
A review of graph neural networks and pretrained language models for knowledge graph reasoning
J Ma, B Liu, K Li, C Li, F Zhang, X Luo, Y Qiao - Neurocomputing, 2024 - Elsevier
Abstract Knowledge Graph (KG) stores human knowledge facts in an intuitive graphical
structure but faces challenges such as incomplete construction or inability to handle new …
structure but faces challenges such as incomplete construction or inability to handle new …
Pasca: A graph neural architecture search system under the scalable paradigm
Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-
based tasks. However, as mainstream GNNs are designed based on the neural message …
based tasks. However, as mainstream GNNs are designed based on the neural message …
Model degradation hinders deep graph neural networks
Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks.
However, drastic performance degradation is always observed when a GNN is stacked with …
However, drastic performance degradation is always observed when a GNN is stacked with …
A geometric deep learning framework for drug repositioning over heterogeneous information networks
Drug repositioning (DR) is a promising strategy to discover new indicators of approved
drugs with artificial intelligence techniques, thus improving traditional drug discovery and …
drugs with artificial intelligence techniques, thus improving traditional drug discovery and …
Regulation-aware graph learning for drug repositioning over heterogeneous biological network
Drug repositioning (DR) is crucial for identifying new disease indications for existing drugs
and enhancing their clinical utility. Despite the effectiveness of various artificial intelligence …
and enhancing their clinical utility. Despite the effectiveness of various artificial intelligence …
Node-wise diffusion for scalable graph learning
Graph Neural Networks (GNNs) have shown superior performance for semi-supervised
learning of numerous web applications, such as classification on web services and pages …
learning of numerous web applications, such as classification on web services and pages …