# Finding compact communities in large graphs

@article{Creusefond2015FindingCC, title={Finding compact communities in large graphs}, author={Jean Creusefond and Thomas Largillier and Sylvain Peyronnet}, journal={2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)}, year={2015}, pages={1457-1464} }

This article presents an efficient hierarchical clustering algorithm that solves the problem of core community detection. It is a variant of the standard community detection problem in which we are particularly interested in the connected core of communities. To provide a solution to this problem, we question standard definitions on communities and provide alternatives. We propose a function called compactness, designed to assess the quality of a solution to this problem. Our algorithm is based… Expand

#### 8 Citations

A LexDFS-Based Approach on Finding Compact Communities

- Computer Science
- 2017

An efficient hierarchical clustering algorithm based on a graph traversal algorithm called LexDFS, which has the property of going through the clustered parts of the graph in a small number of iterations, making them recognisable. Expand

Graph Clustering Via Intra-Cluster Density Maximization

- Computer Science
- 2018

This article forms the clustering problem as a combinatorial optimization problem that maximizes intra-cluster density, a statistically meaningful quantity, and requires the number of clusters, a softbound on cluster size and a penalty coefficient as parameter inputs. Expand

Metrics for Community Analysis: A Survey

- Computer Science
- 2016

A comprehensive and structured overview of the start-of-the-art metrics used for the detection and the evaluation of community structure and conducts experiments on synthetic and real-world networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure. Expand

Metrics for Community Analysis

- Computer Science, Physics
- ACM Comput. Surv.
- 2017

A survey of the start-of-the-art metrics used for the detection and the evaluation of community structure and a comparative analysis of these metrics in measuring the goodness of the underlying community structure is presented. Expand

Community detection methods can discover better structural clusters than ground-truth communities

- Computer Science
- 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
- 2017

Most common methods in the literature for community detection are brought together in a comparative approach and their performances in both real-world networks and synthetic networks are revealed. Expand

On the Evaluation Potential of Quality Functions in Community Detection for Different Contexts

- Computer Science, Physics
- NetSci-X
- 2016

A general methodology is applied to identify different contexts, i.e. groups of graphs where the quality functions behave similarly, and identifies the best quality functions whose results are consistent with expectations from real life applications. Expand

Silhouette for the Evaluation of Community Structures in Multiplex Networks

- Computer Science
- 2018

A versatile definition of the silhouette is proposed, by generalizing it to encompass different scenarios of proximity between entities in a network, where the distance notion can be geodesic-based or homophily-oriented. Expand

Caractériser et détecter les communautés dans les réseaux sociaux. (Characterising and detecting communities in social networks)

- Political Science, Computer Science
- 2017

Mets a disposition un logiciel nomme CoDACom (Community Detection Algorithm Comparator, codacom.greyc.fr) permettant d'appliquer cette methodologie ainsi that d'utiliser un grand nombre d'outils de detection de communautes. Expand

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