Graphene can hold multiple states of superconductivity, a new study finds

The ordinary graphite in pencil lead is proving to be surprisingly multifaceted at the microscale. In a study published in ...
CoinDesk

The Runes revival: Bitcoin traffic hits a two-year high as transactions blast past 820,000

Bitcoin processed more than 820,000 transactions, its highest daily count in over two years, with Rune-related activity accounting for a significant share of network usage. Transactions carrying Rune ...
AlphaGalileo

AdapGNN: A New Framework Enhances Explainability of Graph Neural Networks for Molecular Property Prediction

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
IEEE

M2GCNet: Multi-Modal Graph Convolution Network for Precise Brain Tumor Segmentation Across Multiple MRI Sequences

Abstract: Accurate segmentation of brain tumors across multiple MRI sequences is essential for diagnosis, treatment planning, and clinical decision-making. In this paper, I propose a cutting-edge ...
PNAS

Homophily modulates double descent generalization in graph convolution networks

Graph neural networks (GNNs) have been applied with great success across science and engineering, but we do not understand why they work so well. Motivated by experimental evidence of a rich phase ...
Nature

Spatiotemporal adaptive attention graph convolution network for city-level air quality prediction

Air pollution is a leading cause of human diseases. Accurate air quality predictions are critical to human health. However, it is difficult to extract spatiotemporal features among complex ...
Frontiers

Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury

Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can ...
Frontiers

Efficient graph convolutional networks for seizure prediction using scalp EEG

Epilepsy is a chronic brain disease that causes persistent and severe damage to the physical and mental health of patients. Daily effective prediction of epileptic seizures is crucial for epilepsy ...
Nature

Graph convolutional networks fusing motif-structure information

With the advent of the wave of big data, the generation of more and more graph data brings great pressure to the traditional deep learning model. The birth of graph neural network fill the gap of deep ...
IEEE

Causal Incremental Graph Convolution for Recommender System Retraining

Abstract: The real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN)-based ...