Search · four archives
Search · four archives
5 papers · ranked by Valyu relevance
Qingju Jiao, Peige Zhao, Hanjin Zhang, Yahong Han + 2 more
'Toqir Rana'] Most current graph neural networks (GNNs) are designed from the view of methodology and rarely consider the inherent characters of graph. Although the inherent characters may impact the performance of GNNs, very few methods are proposed to resolve the issue. In this work, we mainly focus on improving the…
Qingju Jiao, Han Zhang, Jingwen Wu, Nan Wang + 3 more
'Yongge Liu' 'Xiao Luo'] Graph neural networks (GNNs), with their ability to incorporate node features into graph learning, have achieved impressive performance in many graph analysis tasks. However, current GNNs including the popular graph convolutional network (GCN) cannot obtain competitive results on the graphs…
Cailum Stienstra, Liam Hebert, Patrick Thomas, Alexander Haack + 2 more
Given that Infrared (IR) spectroscopy is a crucial tool in various chemical and forensic domains, improved in silico methods for predicting experimental spectra are needed due to the time and accuracy limitations of ab initio methods. We employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra…
Arpit Merchant, Ananth Mahadevan, Michael Mathioudakis, Adam Lipowski
'Adam Lipowski'] The task of node classification concerns a network where nodes are associated with labels, but labels are known only for some of the nodes. The task consists of inferring the unknown labels given the known node labels, the structure of the network, and other known node attributes. Common node…
Syeda Akter, Lawrence Holder
IoT sensor networks have an inherent graph structure that can be used to extract graphical features for improving performance in a variety of prediction tasks. We propose a framework that represents IoT sensor network data as a graph, extracts graphical features, and applies feature selection methods to identify the…