While many existing graph neural networks (GNNs) have been proven to perform $\ell_2 $-
based graph smoothing that enforces smoothness globally, in this work we aim to further …

Spectral clustering refers to a family of well-known unsupervised learning algorithms. Rather
than attempting to cluster points in their native domain, one constructs a (usually sparse) …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

Combinatorial optimization (CO) problems are notoriously challenging for neural networks,
especially in the absence of labeled instances. This work proposes an unsupervised …

Geographically annotated social media is extremely valuable for modern information
retrieval. However, when researchers can only access publicly-visible data, one quickly …

We consider point clouds obtained as random samples of a measure on a Euclidean
domain. A graph representing the point cloud is obtained by assigning weights to edges …

We present two graph-based algorithms for multiclass segmentation of high-dimensional
data on graphs. The algorithms use a diffuse interface model based on the Ginzburg …

The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst
supervised deep learning methods rely upon huge amounts of labelled data, the critical …

Abstract Recently proposed Graph Neural Networks (GNNs) for vertex clustering are trained
with an unsupervised minimum cut objective, approximated by a Spectral Clustering (SC) …

We present a novel method for classification of Synthetic Aperture Radar (SAR) data by
combining ideas from graph-based learning and neural network methods within an active …