Journal ArticleDOI
Metrics for Community Analysis: A Survey
TLDR
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.Abstract:
Detecting and analyzing dense groups or communities from social and information networks has attracted immense attention over the last 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 even more complicated due to the fact that communities emerge in the network in various forms such as disjoint, overlapping, and hierarchical. Various heuristics have been proposed to address these challenges, depending on 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. Over the last decade, a large number of such metrics have been proposed. Thus, there arises a need for an organized and detailed survey of the metrics proposed for community detection and evaluation. Here, we present a survey 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 networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.read more
Citations
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Proceedings ArticleDOI
Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection
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TL;DR: An exhaustive search of known methods is performed and a classification of them based on when and how structure and attributes are fused is proposed, which pays attention to available information which methods outperform others and which datasets and quality measures are used for their evaluation.
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Community detection in large-scale social networks: state-of-the-art and future directions
TL;DR: The main goal of this paper is to give a comprehensive survey of community detection algorithms in social graphs based on the computational nature (either centralized or distributed) and thus in static and dynamic social networks.
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