As the world races toward carbon neutrality, electric vehicles (EVs) have emerged as a cornerstone of sustainable transportation, particularly in developing nations like China. However, simply ...
Department of Mathematics and Statistics, Qinghai Minzu University, Xining, China. Since then, much attention has been paid to this topic, but they mainly focus on undirected graphs and integral trees ...
Abstract: Recently, polynomial graph filter learning (PGFL) has demonstrated promising performance for modeling graph signals in Graph Neural Networks (GNNs) on both homophilic and heterophilic graphs ...
1 Department of Mathematics and Applied Mathematics, Sefako Makgatho Health Sciences University, Pretoria, South Africa 2 Department of Mathematics, Usmanu Danfodiyo University, Sokoto, Nigeria ...
# # 'expand > slide': [69.306931, 56.435644, 66.336634, 64.356436, 46.534653, 52.475248, 64.356436, 41.584158, 60.396040], # # 'expand > dyadic': [71.287129, 52. ...
Abstract: In this paper, the design and application of Hermite polynomial graph filter is studied. First, the basics of graph signal processing (GSP) is briefly reviewed and the design problem of ...
App Science, a Sabio Holdings company, today announced it has entered into a multi-year contract with Pivot Marketing Group to support clients including Toyota Motor North America. Analyzing mobile ...
Graph Neural Networks (GNNs) exploit signals from node features and the input graph topology to improve node classification task performance. Recently proposed GNNs work across a variety of homophilic ...
If you find this work useful, please cite our paper. Note that the first three authors contributed equally to this work. Graph Neural Networks (GNNs) exploit signals from node features and the input ...
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