Expanding the horizons of machine learning in nanomaterials to chiral nanostructures
Abstract Machine learning holds significant research potential in the field of nanotechnology,
enabling nanomaterial structure and property predictions, facilitating materials design and …
enabling nanomaterial structure and property predictions, facilitating materials design and …
A novel approach for brain tumour detection using deep learning based technique
Identifying the tumour's extent is a major challenge in planning treatment for brain tumours
and correctly measuring their size. Magnetic resonance imaging (MRI) has emerged as a …
and correctly measuring their size. Magnetic resonance imaging (MRI) has emerged as a …
Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns
Graph neural networks (GNNs) have achieved tremendous success on multiple graph-
based learning tasks by fusing network structure and node features. Modern GNN models …
based learning tasks by fusing network structure and node features. Modern GNN models …
Specformer: Spectral graph neural networks meet transformers
Spectral graph neural networks (GNNs) learn graph representations via spectral-domain
graph convolutions. However, most existing spectral graph filters are scalar-to-scalar …
graph convolutions. However, most existing spectral graph filters are scalar-to-scalar …
How powerful is graph convolution for recommendation?
Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms
for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical …
for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical …
Graph filters for signal processing and machine learning on graphs
Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …
that reside on Euclidean domains, filters are the crux of many signal processing and …
A review of graph-powered data quality applications for IoT monitoring sensor networks
The development of Internet of Things (IoT) technologies has led to the widespread adoption
of monitoring networks for a wide variety of applications, such as smart cities, environmental …
of monitoring networks for a wide variety of applications, such as smart cities, environmental …
Reconstruction of time-varying graph signals via Sobolev smoothness
Graph Signal Processing (GSP) is an emerging research field that extends the concepts of
digital signal processing to graphs. GSP has numerous applications in different areas such …
digital signal processing to graphs. GSP has numerous applications in different areas such …
A survey on spectral graph neural networks
Graph neural networks (GNNs) have attracted considerable attention from the research
community. It is well established that GNNs are usually roughly divided into spatial and …
community. It is well established that GNNs are usually roughly divided into spatial and …
Graph signal processing for geometric data and beyond: Theory and applications
Geometric data acquired from real-world scenes, eg, 2D depth images, 3D point clouds, and
4D dynamic point clouds, have found a wide range of applications including immersive …
4D dynamic point clouds, have found a wide range of applications including immersive …