Template-Based Graph Clustering

07/05/2021
by   Mateus Riva, et al.
0

We propose a novel graph clustering method guided by additional information on the underlying structure of the clusters (or communities). The problem is formulated as the matching of a graph to a template with smaller dimension, hence matching n vertices of the observed graph (to be clustered) to the k vertices of a template graph, using its edges as support information, and relaxed on the set of orthonormal matrices in order to find a k dimensional embedding. With relevant priors that encode the density of the clusters and their relationships, our method outperforms classical methods, especially for challenging cases.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/28/2010

Many-to-Many Graph Matching: a Continuous Relaxation Approach

Graphs provide an efficient tool for object representation in various co...
research
03/18/2019

QATM: Quality-Aware Template Matching For Deep Learning

Finding a template in a search image is one of the core problems many co...
research
09/01/2021

A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection

Cough is a major symptom of respiratory-related diseases. There exists a...
research
11/11/2013

Notes on Elementary Spectral Graph Theory. Applications to Graph Clustering Using Normalized Cuts

These are notes on the method of normalized graph cuts and its applicati...
research
06/21/2018

Sampling Clustering

We propose an efficient graph-based divisive cluster analysis approach c...
research
07/24/2020

Scaling Graph Clustering with Distributed Sketches

The unsupervised learning of community structure, in particular the part...
research
09/20/2021

Network Clustering by Embedding of Attribute-augmented Graphs

In this paper we propose a new approach to detect clusters in undirected...

Please sign up or login with your details

Forgot password? Click here to reset