Finding compact communities in large graphs

Creusefond, Jean; Largillier, Thomas; Peyronnet, Sylvain

doi:10.1145/2808797.2808868

Cited by 7 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ Creusefond et al 2014] used compactness which measures the potential speed of a diffusion process in a community. Starting from the most eccentric node, the function captures the number of edges reached per time step by a perfect transmission of information.…”

Section: Wmentioning

confidence: 99%

Metrics for Community Analysis

et al. 2017

View full text Add to dashboard Cite

Detecting and analyzing dense groups or communities from social and information networks has attracted immense attention over last one decade due to its enormous applicability in different domains. Community detection is an ill-defined problem, as the nature of the communities is not known in advance. The problem has turned out to be even complicated due to the fact that communities emerge in the network in various forms -disjoint, overlapping, hierarchical etc. Various heuristics have been proposed depending upon the application in hand. All these heuristics have been materialized in the form of new metrics, which in most cases are used as optimization functions for detecting the community structure, or provide an indication of the goodness of detected communities during evaluation. There arises a need for an organized and detailed survey of the metrics proposed with respect to community detection and evaluation. In this survey, we present a comprehensive and structured overview of the start-of-the-art metrics used for the detection and the evaluation of community structure. We also conduct experiments on synthetic and real-world networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.

show abstract

Section: Wmentioning

confidence: 99%

Metrics for Community Analysis

et al. 2017

View full text Add to dashboard Cite

show abstract

“…It is measured as the ratio of internal degree to the total degree of a community: [11] captures the idea that nodes in a community must be well connected. It quantifies the fraction of edges inside S over the total possible edges could be established in S: [29] suggests that good communities should be at the same time dense and easily reachable from nodes to nodes. This quality is calculated by:…”

Section: A Definitions Of Structural Goodness Metricsmentioning

confidence: 99%

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

Dao

Bothorel

Lenca

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

View full text Add to dashboard Cite

International audienceCommunity detection emerged as an important exploratory task in complex networks analysis across many scientific domains. Many methods have been proposed to solve this problem, each one with its own mechanism and sometimes with a different notion of community. In this article, we bring most common methods in the literature together in a comparative approach and reveal their performances in both real-world networks and synthetic networks. Surprisingly, many of those methods discovered better communities than the declared ground-truth communities in terms of some topological goodness features, even on benchmarking networks with built-in communities. We illustrate different structural characteristics that these methods could identify in order to support users to choose an appropriate method according to their specific requirements on different structural qualities

show abstract

“…The Compactness [11] measures the potential speed of a diffusion process in a community. Starting from the most eccentric node, the function captures the number of edges reached per time step by a perfect transmission of information.…”

Section: Quality Functionsmentioning

confidence: 99%

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

Creusefond¹,

Largillier²,

Peyronnet³

2016

Advances in Network Science

Self Cite

View full text Add to dashboard Cite

Abstract. Due to nowadays networks' sizes, the evaluation of a community detection algorithm can only be done using quality functions. These functions measure different networks/graphs structural properties, each of them corresponding to a different definition of a community. Since there exists many definitions for a community, choosing a quality function may be a difficult task, even if the networks' statistics/origins can give some clues about which one to choose. In this paper, we apply a general methodology to identify different contexts, i.e. groups of graphs where the quality functions behave similarly. In these contexts we identify the best quality functions, i.e. quality functions whose results are consistent with expectations from real life applications.

show abstract

Finding compact communities in large graphs

Cited by 7 publications

References 21 publications

Metrics for Community Analysis

Metrics for Community Analysis

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

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

Contact Info

Product

Resources

About