Comparative Evaluation of Community Detection Algorithms: A Topological Approach
TLDR
A comprehensive comparative study of a representative set of community detection methods, in which community-oriented topological measures are used to qualify the communities and evaluate their deviation from the reference structure and it turns out there is no equivalence between the two approaches.Abstract:
Community detection is one of the most active fields in complex networks analysis, due to its potential value in practical applications. Many works inspired by different paradigms are devoted to the development of algorithmic solutions allowing to reveal the network structure in such cohesive subgroups. Comparative studies reported in the literature usually rely on a performance measure considering the community structure as a partition (Rand Index, Normalized Mutual information, etc.). However, this type of comparison neglects the topological properties of the communities. In this article, we present a comprehensive comparative study of a representative set of community detection methods, in which we adopt both types of evaluation. Community-oriented topological measures are used to qualify the communities and evaluate their deviation from the reference structure. In order to mimic real-world systems, we use artificially generated realistic networks. It turns out there is no equivalence between both approaches: a high performance does not necessarily correspond to correct topological properties, and vice-versa. They can therefore be considered as complementary, and we recommend applying both of them in order to perform a complete and accurate assessment.read more
Citations
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Journal ArticleDOI
Community detection in networks: A multidisciplinary review
Muhammad Aqib Javed,Muhammad Shahzad Younis,Siddique Latif,Siddique Latif,Junaid Qadir,Adeel Baig,Adeel Baig +6 more
TL;DR: A contemporary survey on the methods of community detection and its applications in the various domains of real life by reviewing prevailing community detection algorithms that range from traditional algorithms to state of the art algorithms for overlapping community detection.
Journal ArticleDOI
Community detection in large‐scale networks: a survey and empirical evaluation
Steve Harenberg,Steve Harenberg,Gonzalo A. Bello,Gonzalo A. Bello,L. Gjeltema,L. Gjeltema,Stephen Ranshous,Stephen Ranshous,Jitendra K. Harlalka,Jitendra K. Harlalka,Ramona G. Seay,Ramona G. Seay,Kanchana Padmanabhan,Kanchana Padmanabhan,Nagiza F. Samatova,Nagiza F. Samatova +15 more
TL;DR: This review evaluated eight state‐of‐the‐art and five traditional algorithms for overlapping and disjoint community detection on large‐scale real‐world networks with known ground‐truth communities and showed that these two types of metrics are not equivalent.
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Metrics for Community Analysis: A Survey
TL;DR: A survey of the metrics used for community detection and evaluation can be found in this paper, where the authors also conduct experiments on synthetic and real networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.
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Opinion leader detection: A methodological review
TL;DR: This review of the well-known techniques for opinion leader detection problems is classified into descriptive approaches, statistical and stochastic methods, diffusion process based approaches, topological based methods, data mining and learning methods, and approaches based on hybrid content mining.
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CDLIB : a python library to extract, compare and evaluate communities from complex networks
TL;DR: 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.
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