IEEE

Uncertainty-Aware Graph Neural Networks: A Multihop Evidence Fusion Approach

Abstract: Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features. Existing GNNs, unfortunately, fail to account for the uncertainty of ...
Microsoft

Heterogeneous Graph Neural Network on Semantic Tree

The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs), since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, ...
C&EN

Multilevel Fusion Graph Neural Network for Molecule Property Prediction

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...
IEEE

Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization

Abstract: In this paper, we design Graph Neural Networks (GNNs) with attention mechanisms to tackle an important yet challenging nonlinear regression problem: massive network localization. We first ...
Scientific Research Publishing

An In-Depth Analysis of Graph Neural Networks and Machine Learning Approaches for Drug Repositioning ()

1 Department of Computer Science and Engineering, Kishoreganj University, Kishoreganj, Bangladesh. 2 Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh. 3 ...
EurekAlert!

Real-time, large-scale graph neural network inference through BingoCGN

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...
InfoWorld

Running PyTorch on an Arm Copilot+ PC

When Microsoft launched its Copilot+ PC range almost a year ago, it announced that it would deliver the Copilot Runtime, a set of tools to help developers take advantage of the devices’ built-in AI ...