Related Experiment Video
Updated: Mar 21, 2026

Analysis of SEC-SAXS data via EFA deconvolution and Scatter
Published on: January 28, 2021
Disentangling giant component and finite cluster contributions in sparse random matrix spectra
1Mathematics Department, King's College London, Strand, London WC2R 2LS, United Kingdom.
Abstract:
We describe a method for disentangling giant component and finite cluster contributions to sparse random matrix spectra, using sparse symmetric random matrices defined on Erdős-Rényi graphs as an example and test bed. Our methods apply to sparse matrices defined in terms of arbitrary graphs in the configuration model class, as long as they have finite mean degree.
Related Concept Videos
Vector Algebra: Method of Components
In many applications, the magnitudes and directions of...
Extraction: Partition and Distribution Coefficients
For extracting a solute from an aqueous phase into an...
¹³C NMR: ¹H–¹³C Decoupling
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
¹H NMR: Interpreting Distorted and Overlapping Signals
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
Mass Spectrometry: Complex Analysis
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
Cluster Sampling Method
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

