A Comparative Evaluation of Community Detection Algorithms in Social Networks

George, Roy; Shujaee, K.; Kerwat, Mohamed; Felfli, Zineb; Gelenbe, D.; Ukuwu, K.

doi:10.1016/j.procs.2020.04.124

Cited by 15 publications

(9 citation statements)

References 27 publications

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“…ere are many measures [48][49][50][51] which can be used to assess the detecting ability of the algorithms, and almost all of them accomplish this purpose via measuring the quality of the detected community structures from different perspectives. In this paper, we use three widely used metrics, say the modularity (Q) [4], the NMI [52], and the ratio of the detected number of communities to the real number of communities (R) [6], to evaluate the quality of the detected communities.…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Node Similarity and Community Link Strength‐Based Community Discovery Algorithm

et al. 2021

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Community structure is one of the common characteristics of complex networks. In the practical work, we have noted that every node and its most similar node tend to be assigned to the same community and that two communities are often merged together if there exist relatively more edges between them. Inspired by these observations, we present a community-detection method named NSCLS in this paper. Firstly, we calculate the similarities between any node and its first- and second-order neighbors in a novel way and then extract the initial communities from the network by allocating every node and its most similar node to the same community. In this procedure, some nodes located at the community boundaries might be classified in the incorrect communities. To make a redemption, we adjust their community affiliations by reclassifying each of them into the community in which most of its neighbors have been. After that, there might exist relatively larger number of edges between some communities. Therefore, we consider to merge such communities to improve the quality of the final community structure further. To this end, we calculate the link strength between communities and merge some densely connected communities based on this index. We evaluate NSCLS on both some synthetic networks and some real-world networks and show that it can detect high-quality community structures from various networks, and its results are much better than the counterparts of comparison algorithms.

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Section: Evaluation Metricsmentioning

confidence: 99%

A Node Similarity and Community Link Strength‐Based Community Discovery Algorithm

et al. 2021

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“…However, on the other hand, when dealing with a large network graph, this algorithm is not particularly efficient and not recommended for large data sets. Moreover, the Girvan and Newman algorithm has time complexity O(m 2 n) which make this algorithm relatively slow and time costing as compared to the Louvain and Infomap community detection method [16], [24], [27], [28].…”

Section: Nomentioning

confidence: 99%

CDRA: A Community Detection based Routing Algorithm for Link Failure Recovery in Software Defined Networks

Daha¹,

Zahid²,

Isyaku³

et al. 2021

IJACSA

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The increase in size and complexity of the Internet has led to the introduction of Software Defined Networking (SDN). SDN is a new networking paradigm that breaks the limitations of traditional IP networks and upgrades the current network infrastructures. However, like traditional IP networks, network failures may also occur in SDN. Multiple research studies have discussed this problem by using a variety of techniques. Among them is the use of the community detection method is one of the failure recovery technique for SDN. However, this technique have not considered the specific problem of multiple link multi-community failure and inter-community link failure scenarios. This paper presents a community detection-based routing algorithm (CDRA) for link failure recovery in SDN. The proposed CDRA scheme is efficient to deal with single link intra-community failure scenarios and multiple link multi-community failure scenarios and is also able to handle the inter-community link failure scenarios in SDN. Extensive simulations are performed to evaluate the performance of the proposed CDRA scheme. The simulation results depicts that the proposed CDRA scheme have better simulations results and reduce average round trip time by 35.73%, avg data packet loss by 1.26% and average end to end delay 49.3% than the Dijkstra based general recovery algorithm and also can be used on a large scale network platform.

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“…One can also evaluate the community detection algorithms using dynamic graphs with planted evolving community structure, as a benchmark [10]. In another study [11] the performance of the Community Density Rank (CDR) algorithm against other community detection algorithms were compared using synthetic data from Lancichinetti, Fortunato and Radicchi (LFR) algorithm [12]. Dao and the team also have done a study [13] to compare community detection methods, based on computation time and community size distribution.…”

Section: Introductionmentioning

confidence: 99%

Assessing the impact of the density and sparsity of the network on community detection using a Gaussian mixture random partition graph generator

Wickramasinghe

Muthukumarana

2022

Int. j. inf. tecnol.

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Identification of sub-networks within a network is essential to understand the functionality of a network. This process is called as 'Community detection'. There are various existing community detection algorithms, and the performance of these algorithms can be varied based on the network structure. In this paper, we introduce a novel random graph generator using a mixture of Gaussian distributions. The community sizes of the generated network depend on the given Gaussian distributions. We then develop simulation studies to understand the impact of density and sparsity of the network on community detection. We use Infomap, Label propagation, Spinglass, and Louvain algorithms to detect communities. The similarity between true communities and detected communities is evaluated using Adjusted Rand Index, Adjusted Mutual Information, and Normalized Mutual Information similarity scores. We also develop a method to generate heatmaps to compare those similarity score values. The results indicate that the Louvain algorithm has the highest capacity to detect perfect communities while Label Propagation has the lowest capacity

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A Comparative Evaluation of Community Detection Algorithms in Social Networks

Cited by 15 publications

References 27 publications

A Node Similarity and Community Link Strength‐Based Community Discovery Algorithm

A Node Similarity and Community Link Strength‐Based Community Discovery Algorithm

CDRA: A Community Detection based Routing Algorithm for Link Failure Recovery in Software Defined Networks

Assessing the impact of the density and sparsity of the network on community detection using a Gaussian mixture random partition graph generator

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