Organizational Chart: Types, Meaning, and How It Works

Organizational charts can be broad-based, depicting the overall company, or can be department- or unit-specific, focusing on ...
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 ...
Quanta Magazine

New Proof Settles Decades-Old Bet About Connected Networks

According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong. It started with a bet. In the late 1980s, at a ...
IEEE

Graph-Based Semi-Supervised Deep Image Clustering With Adaptive Adjacency Matrix

Abstract: Image clustering is a research hotspot in machine learning and computer vision. Existing graph-based semi-supervised deep clustering methods suffer from three problems: 1) because clustering ...
IEEE

Contrastive Graph Convolutional Networks With Generative Adjacency Matrix

Abstract: Semi-supervised node classification with Graph Convolutional Network (GCN) is an attractive topic in social media analysis and applications. Recent studies show that GCN-based classification ...
Frontiers

Spatial-Temporal Attention Mechanism and Graph Convolutional Networks for Destination Prediction

Urban transportation destination prediction is a crucial issue in the area of intelligent transportation, such as urban traffic planning and traffic congestion control. The spatial structure of the ...
PNAS

Spectra of random graphs with given expected degrees

In the study of the spectra of power-law graphs, there are basically two competing approaches. One is to prove analogues of Wigner's semicircle law, whereas the other predicts that the eigenvalues ...