Over the past few years, there has been a substantial effort towards automated detection of
fake news on social media platforms. Existing research has modeled the structure, style …

Graph contrastive learning (GCL) shows excellent potential in unsupervised graph
representation learning. Data augmentation (DA), responsible for generating diverse views …

The assessment and classification of architectural sectional drawings is critical in the
architecture, engineering, and construction (AEC) field, where the accurate representation of …

Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the
expressiveness of graph neural networks (GNNs), showing that the neighborhood …

Although existing deep learning models perform remarkably well at predicting
physicochemical properties and binding affinities, the generation of new molecules with …

In graph neural networks (GNNs), both node features and labels are examples of graph
signals, a key notion in graph signal processing (GSP). While it is common in GSP to impose …

This paper introduces Tensor Visibility Graph-enhanced Attention Networks (TVGeAN), a
novel graph autoencoder model specifically designed for MTS learning tasks. The …

Graph neural networks have been successful in many learning problems and real-world
applications. A recent line of research explores the power of graph neural networks to solve …

Community models for malicious content detection, which take into account the context from
a social graph alongside the content itself, have shown remarkable performance on …

With the end of Moore's Law, there is a growing demand for rapid architectural innovations
in modern processors, such as RISC-V custom extensions, to continue performance scaling …