A comprehensive literature review on community detection: Approaches and applications

El-Moussaoui, Mohamed; Agouti, Tarik; Tikniouine, Abdessadek; Adnani, Mohamed El

doi:10.1016/j.procs.2019.04.042

Cited by 42 publications

(17 citation statements)

References 33 publications

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“…Since the the number of such partitions grows exponentially in the number of nodes, the literature of community detection is focused on the complexity of the algorithms applied. A recent survey paper [18] offers a valuable overview on the field of community detection. According to [18], the complexity of detecting communities depends on the nature of communities (static, dynamic, overlapping), as well as the network's topology (weighted, and/or directed graphs).…”

Section: Community Detection Methodsmentioning

confidence: 99%

“…A recent survey paper [18] offers a valuable overview on the field of community detection. According to [18], the complexity of detecting communities depends on the nature of communities (static, dynamic, overlapping), as well as the network's topology (weighted, and/or directed graphs). One could consider the problem as a clustering of the nodes, based on similarity measures in the network [19]; or it may be worth trying to ignore the edge direction as well [20]; or one could focus on determining leader nodes based on centrality measures and build the communities around the leaders [21].…”

Section: Community Detection Methodsmentioning

confidence: 99%

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Network Analysis Based on Important Node Selection and Community Detection

et al. 2021

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The stability and robustness of a complex network can be significantly improved by determining important nodes and by analyzing their tendency to group into clusters. Several centrality measures for evaluating the importance of a node in a complex network exist in the literature, each one focusing on a different perspective. Community detection algorithms can be used to determine clusters of nodes based on the network structure. This paper shows by empirical means that node importance can be evaluated by a dual perspective—by combining the traditional centrality measures regarding the whole network as one unit, and by analyzing the node clusters yielded by community detection. Not only do these approaches offer overlapping results but also complementary information regarding the top important nodes. To confirm this mechanism, we performed experiments for synthetic and real-world networks and the results indicate the interesting relation between important nodes on community and network level.

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Section: Community Detection Methodsmentioning

confidence: 99%

Section: Community Detection Methodsmentioning

confidence: 99%

Network Analysis Based on Important Node Selection and Community Detection

et al. 2021

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“…Reviews that are related to community detection methods and approaches in real‐life networks: El‐Moussaoui et al (2019) reviewed literature on community detection for complex networks. They compared between approaches applied on datasets in different studies from different domains without focusing on E‐learning environments. Javed et al (2018) prepared a comprehensive review on the community detection algorithms in networks.…”

Section: Purpose Of the Literature Reviewmentioning

confidence: 99%

Detecting communities using social network analysis in online learning environments: Systematic literature review

Yassine¹,

Kadry²,

Sicilia³

2021

WIREs Data Min & Knowl

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Uncovering community structure has made a significant advancement in explaining, analyzing, and forecasting behaviors and dynamics of networks related to different fields in sociology, criminology, biology, medicine, communication, economics, and academia. Detecting and clustering communities is a powerful step toward identifying the structural properties and the behavioral patterns in social networks. Recently, online learning has been progressively adopted by a lot of educational practices which raise many questions about assessing the learners' engagement, collaboration, and behaviors in the new emerging learning communities. This systematic literature review aims to assess the use of community detection techniques in analyzing the network's structure in online learning environments. It provides a comprehensive overview of the existing research that adopted those techniques with identifying the educational objectives behind their application as well as suggesting possible future research directions. Our analysis covered 65 studies that found in the literature and applied different community discovery techniques on various types of online learning environments to analyze their users' interactions patterns. Our review revealed the potential of this field in improving educational practices and decisions and in utilizing the massive amount of data generated from interacting with those environments. Finally, we highlighted the need to include automated community discovery techniques in online learning environments to facilitate and enhance their use as well as we stressed on the urge for further advance research to uncover a lot of hidden opportunities. This article is categorized under: Algorithmic Development > Statistics Algorithmic Development > Web Mining Application Areas > Education and Learning

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“…An attractive property that is worth investigating in this context is the community structure [54], i.e., the division of the network into groups of vertices that are similar among themselves but dissimilar from the rest of the network. The capability to detect the partitioning of a network into communities can give valuable insights into the organization and behavior of the system that the network models [34,35]. In this particular case, the topology of the so-built hypergraph suggests clusters of businesses that users commonly review together.…”

Section: Exploring and Analyzing User Reviews: Yelpcommentioning

confidence: 99%

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

Spagnuolo¹,

Cordasco²,

Szufel³

et al. 2020

Internet Mathematics

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Real-world complex networks are usually being modeled as graphs. The concept of graphs assumes that the relations within the network are binary (for instance, between pairs of nodes); however, this is not always true for many real-life scenarios, such as peer-to-peer communication schemes, paper co-authorship, or social network interactions. For such scenarios, it is often the case that the underlying network is better and more naturally modeled by hypergraphs. A hypergraph is a generalization of a graph in which a single (hyper)edge can connect any number of vertices. Hypergraphs allow modelers to have a complete representation of multi-relational (many-to-many) networks; hence, they are extremely suitable for analyzing and discovering more subtle dependencies in such data structures. Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (such as searching or traversing the network) to computing significant hypergraph measures, to including more challenging algorithms (such as community detection). In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs and propose two new algorithms for analyzing their properties: s-betweenness and modified label propagation. We also present various approaches for hypergraph visualization integrated into our tool. In order to demonstrate how to exploit the library in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series. The results are promising and they confirm the ability of hypergraphs to provide more insight than standard graph-based approaches.

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A comprehensive literature review on community detection: Approaches and applications

Cited by 42 publications

References 33 publications

Network Analysis Based on Important Node Selection and Community Detection

Network Analysis Based on Important Node Selection and Community Detection

Detecting communities using social network analysis in online learning environments: Systematic literature review

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

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