Dense and sparse vertex connectivity in networks

06/10/2020
by   Djellabi Mehdi, et al.
0

The different approaches developed to analyze the structure of complex networks have generated a large number of studies. In the field of social networks at least, studies mainly address the detection and analysis of communities. In this paper, we challenge these approaches and focus on nodes that have meaningful local interactions able to identify the internal organization of communities or the way communities are assembled. We propose an algorithm, ItRich, to identify this type of nodes, based on the decomposition of a graph into successive, less and less dense, layers. Our method is tested on synthetic and real data sets and meshes well with other methods such as community detection or k-core decomposition.

READ FULL TEXT
research
11/02/2010

Community Detection in Networks: The Leader-Follower Algorithm

Traditional spectral clustering methods cannot naturally learn the numbe...
research
07/14/2022

Structure of Core-Periphery Communities

It has been experimentally shown that communities in social networks ten...
research
04/26/2016

Evaluating the effect of topic consideration in identifying communities of rating-based social networks

Finding meaningful communities in social network has attracted the atten...
research
03/27/2023

Chromatic Community Structure Detection

The detection of community structure is probably one of the hottest tren...
research
01/15/2016

Real-Time Community Detection in Large Social Networks on a Laptop

For a broad range of research, governmental and commercial applications ...
research
03/06/2023

Well-Connected Communities in Real-World and Synthetic Networks

Integral to the problem of detecting communities through graph clusterin...
research
05/26/2021

Block Dense Weighted Networks with Augmented Degree Correction

Dense networks with weighted connections often exhibit a community like ...

Please sign up or login with your details

Forgot password? Click here to reset