Diffuse interface models on graphs for classification of high dimensional data
AL Bertozzi, A Flenner - siam REVIEW, 2016 - SIAM
This paper is a republication of an MMS paper [AL Bertozzi and A. Flenner, Multiscale
Model. Simul., 10 (2012), pp. 1090--1118] describing a new class of algorithms for …
Model. Simul., 10 (2012), pp. 1090--1118] describing a new class of algorithms for …
Discriminative metric learning for multi-view graph partitioning
In recent years, multi-view graph partitioning has attracted more and more attention, but
most efforts have been made to develop graph partitioning approaches directly in the …
most efforts have been made to develop graph partitioning approaches directly in the …
Multilayer complex network descriptors for color–texture characterization
A new method based on complex networks is proposed for color–texture analysis. The
proposal consists of modeling the image as a multilayer complex network where each color …
proposal consists of modeling the image as a multilayer complex network where each color …
[HTML][HTML] Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding
The scarcity of high-quality annotations in many application scenarios has recently led to an
increasing interest in devising learning techniques that combine unlabeled data with labeled …
increasing interest in devising learning techniques that combine unlabeled data with labeled …
Asymptotic analysis of the Ginzburg–Landau functional on point clouds
M Thorpe, F Theil - Proceedings of the Royal Society of Edinburgh …, 2019 - cambridge.org
The Ginzburg–Landau functional is a phase transition model which is suitable for
classification type problems. We study the asymptotics of a sequence of Ginzburg–Landau …
classification type problems. We study the asymptotics of a sequence of Ginzburg–Landau …
An MBO method for modularity optimisation based on total variation and signless total variation
Z Li, Y van Gennip, V John - European Journal of Applied …, 2024 - cambridge.org
In network science, one of the significant and challenging subjects is the detection of
communities. Modularity [1] is a measure of community structure that compares connectivity …
communities. Modularity [1] is a measure of community structure that compares connectivity …
User abnormal behavior recommendation via multilayer network
With the growing popularity of online services such as online banking and online shopping,
one of the essential research topics is how to build a privacy-preserving user abnormal …
one of the essential research topics is how to build a privacy-preserving user abnormal …
Superpixels algorithms through network community detection
A Perez - arXiv preprint arXiv:2308.14101, 2023 - arxiv.org
Community detection is a powerful tool from complex networks analysis that finds
applications in various research areas. Several image segmentation methods rely for …
applications in various research areas. Several image segmentation methods rely for …
Modularities maximization in multiplex network analysis using Many-Objective Optimization
Nowadays, social network analysis receives big attention from academia, industries and
governments. Some practical applications such as community detection and centrality in …
governments. Some practical applications such as community detection and centrality in …
Graphical models in machine learning, networks and uncertainty quantification
AL Bertozzi - Proceedings of the International Congress of …, 2018 - World Scientific
This paper is a review article on semi-supervised and unsupervised graph models for
classification using similarity graphs and for community detection in networks. The paper …
classification using similarity graphs and for community detection in networks. The paper …