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

Creusefond, Jean; Largillier, Thomas; Peyronnet, Sylvain

doi:10.1007/978-3-319-28361-6_9

Cited by 18 publications

(15 citation statements)

References 33 publications

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“…The underlying model defines community as a group of people within which communication quickly reaches everyone. They recently used it as a community-level quality function to measure the goodness of the detected community [Creusefond et al 2015].…”

Section: Wmentioning

confidence: 99%

Metrics for Community Analysis

et al. 2017

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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.

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Section: Wmentioning

confidence: 99%

Metrics for Community Analysis

et al. 2017

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“…Given that there is no universal quality metric, Creusefond et al (2016) apply a general methodology to identify different contexts, groups of graphs where the quality functions behave similarly. In these contexts, they identify the most effective quality functions, i.e.…”

Section: Related Workmentioning

confidence: 99%

Community detection algorithm evaluation with ground-truth data

Jebabli

Cherifi

et al. 2018

Physica A: Statistical Mechanics and its Applications

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Community structure is of paramount importance for the understanding of complex networks. Consequently, there is a tremendous effort in order to develop efficient community detection algorithms. Unfortunately, the issue of a fair assessment of these algorithms is a thriving open question. If the ground-truth community structure is available, various clustering-based metrics are used in order to compare it versus the one discovered by these algorithms. However, these metrics defined at the node level are fairly insensitive to the variation of the overall community structure. To overcome these limitations, we propose to exploit the topological features of the 'community graphs' (where the nodes are the communities and the links represent their interactions) in order to evaluate the algorithms. To illustrate our methodology, we conduct a comprehensive analysis of overlapping community detection algorithms using a set of real-world networks with known a priori community structure. Results provide a better perception of their relative performance as compared to classical metrics. Moreover, they show that more emphasis should be put on the topology of the community structure. We also investigate the relationship between the topological properties of the community structure and the alternative evaluation measures (quality metrics and clustering metrics). It appears clearly that they present different views of the community structure and that they must be combined in order to evaluate the effectiveness of community detection algorithms.

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“…Creusefond et al [4] proposed a methodology to identify groups of networks where quality functions perform consistently. The authors analyzed quality functions in three levels of granularity from node-level to community-level and network-level.…”

Section: Related Workmentioning

confidence: 99%

Community Structures Evaluation in Complex Networks: A Descriptive Approach

Dao

Bothorel

Lenca

2017

3rd International Winter School and Conference on Network Science

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Evaluating a network partition just only via conventional quality metrics -such as modularity, conductance or normalized mutual of information -is usually insufficient. Indeed, global quality scores of a network partition or its clusters do not provide many ideas about their structural characteristics. Furthermore, quality metrics often fail to reach an agreement especially in networks whose modular structures are not very obvious. Evaluating the goodness of network partitions in function of desired structural properties is still a challenge. Here, we propose a methodology that allows one to expose structural information of clusters in a network partition in a comprehensive way, thus eventually helps one to compare communities identified by different community detection methods. This descriptive approach also helps to clarify the composition of communities in realworld networks. The methodology hence bring us a step closer to the understanding of modular structures in complex networks.

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

Cited by 18 publications

References 33 publications

Metrics for Community Analysis

Metrics for Community Analysis

Community detection algorithm evaluation with ground-truth data

Community Structures Evaluation in Complex Networks: A Descriptive Approach

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