Spiking Neural Networks (SNNs) represent a new generation of artificial neural networks that draw inspiration from biological systems. However, due to the intricate dynamics they exhibit and the ...

The Minimization of Open Stacks Problem (MOSP) is a Pattern Sequencing Problem that often arises in industry. Besides the MOSP, there are also other related Pattern Sequencing Problems of similar ...

Identity management for cybersecurity is inherently a complex graph problem due to the vast, dynamic, and interconnected nature of modern IT environments.

The problem is motivated by the real-world surface non-revisiting coverage path planning (NCPP) task carried out by manipulators, where the physical meaning of maximising the colouring continuity in ...

This study proposes a general Graph Convolutional Networks (GCN) model architecture for predicting seizures to solve the problem of oversized seizure prediction models based on exploring the graph ...

Graph database query languages are growing, along with graph databases. They let developers ask complex questions and find relationships.

Graph processing at hyperscale has historically been a challenge because of the sheer complexity of algorithms and graph workflows. Alibaba has been tackling this issue via a project called GraphScope ...

Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of ...

After the graph is submitted to the Qatalyst API, which implements familiar NetworkX-type functions, QGraph automatically transforms the graph into a constrained optimization problem based on the ...

Engineers could use this breakthrough in graph theory to design wildly efficient quantum computer chips.