A Novel Approach to Evaluate Community Detection Algorithms on Ground Truth

Rossetti, Giulio; Pappalardo, Luca; Rinzivillo, Salvatore

doi:10.1007/978-3-319-30569-1_10

Cited by 41 publications

(27 citation statements)

References 14 publications

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“…Proof: Obvious if c = c * . Otherwise, since X * is the optimal solution of problem (25), one has: ∀ matrix X of size c × c * , if X is the feasible solution of problem (22), one can create X as follows:…”

Section: Model Descriptionmentioning

confidence: 99%

Evaluation of Community Detection Methods

Liu

Cheng

Zhang

2019

IEEE Trans. Knowl. Data Eng.

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Community structures are critical towards understanding not only the network topology but also how the network functions. However, how to evaluate the quality of detected community structures is still challenging and remains unsolved. The most widely used metric, normalized mutual information (NMI), was proved to have finite size effect, and its improved form relative normalized mutual information (rNMI) has reverse finite size effect. Corrected normalized mutual information (cNMI) was thus proposed and has neither finite size effect nor reverse finite size effect. However, in this paper we show that cNMI violates the so-called proportionality assumption. In addition, NMI-type metrics have the problem of ignoring importance of small communities. Finally, they cannot be used to evaluate a single community of interest. In this paper, we map the computed community labels to the ground-truth ones through integer linear programming, then use kappa index and F-score to evaluate the detected community structures. Experimental results demonstrate the advantages of our method.

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Section: Model Descriptionmentioning

confidence: 99%

Evaluation of Community Detection Methods

Liu

Cheng

Zhang

2019

IEEE Trans. Knowl. Data Eng.

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“…2 × precision×recall precision+recall (Rossetti et al 2016) nf1 Normalized version of F1 that corrects the resemblance score taking into account degree of node overlap and clutering coverage.…”

Section: Ri−expectedmentioning

confidence: 99%

“…Redundancy (Rossetti 2017), (Rossetti et al 2016) nmi Normalized Mutual Information (NMI) is an normalization of the Mutual Information (MI) score to scale the results between 0 (no mutual information) and 1 (perfect correlation) onmi-LFK Original extension of the Normalized Mutual Information (NMI) score to cope with overlapping partitions. onmi-MGH Extension of the Normalized Mutual Information (NMI) score to cope with overlapping partitions, based on max normalization.…”

Section: F1×coveragementioning

confidence: 99%

CDLIB: a python library to extract, compare and evaluate communities from complex networks

2019

Self Cite

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Community Discovery is among the most studied problems in complex network analysis. During the last decade, many algorithms have been proposed to address such task; however, only a few of them have been integrated into a common framework, making it hard to use and compare different solutions. To support developers, researchers and practitioners, in this paper we introduce a python library -namely CDLIB -designed to serve this need. The aim of CDLIB is to allow easy and standardized access to a wide variety of network clustering algorithms, to evaluate and compare the results they provide, and to visualize them. It notably provides the largest available collection of community detection implementations, with a total of 39 algorithms.

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“…Rossetti et al [13] introduce a novel approach to evaluate the efficiency of algorithms by comparing detected communities with a given ground-truth information. They use precision score, recall score and their harmonic mean, known as F1-measure, to assess the quality of community discovery methods using scatter plots.…”

Section: Related Workmentioning

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

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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

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A Novel Approach to Evaluate Community Detection Algorithms on Ground Truth

Cited by 41 publications

References 14 publications

Evaluation of Community Detection Methods

Evaluation of Community Detection Methods

CDLIB: a python library to extract, compare and evaluate communities from complex networks

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

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