Abstract: Despite advancements using graph neural networks (GNNs) to capture complex user-item interactions, challenges persist due to data sparsity and noise. To address these, self-supervised ...

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 ...

A Local-Scale Dataset of Annual Spatiotemporal Maps of Physical Vulnerability in the Cyclone-Impacted Coastal Khurushkul Community (Bangladesh) and Mudslide-Affected Freetown (Sierra Leone) (2016–2023 ...

Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul 34349, Turkey Lab for Innovative Drugs (Lab4IND), ...

Google published details of a new kind of AI based on graphs called a Graph Foundation Model (GFM) that generalizes to previously unseen graphs and delivers a three to forty times boost in precision ...

Imagine standing atop a mountain, gazing at the vast landscape below, trying to make sense of the world around you. For centuries, explorers relied on such vantage points to map their surroundings.

Abstract: Gears play an indispensable role in the transmission systems of advanced industrial equipment. Timely detection of anomalies in gear components is crucial for ensuring operational efficiency ...

This repository provides the implementation of the AEGAE method for community detection in attributed graphs. AEGAE integrates Laplacian regularization and a graph autoencoder to generate robust node ...

The growing microbial resistance to traditional medicines necessitates in-depth analysis of medicine-microbe interactions (MMIs) to develop new therapeutic strategies. Widely used artificial ...

1 School of Computer Science and Technology, Xidian University, Xi'an, China 2 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Electronic ...