Communities detection in social network based on local edge centrality

Li, Xuequn; Zhou, Shuming; Liu, Jiafei; Lian, Guanqin; Chen, Gaolin; Lin, Chen-Wan

doi:10.1016/j.physa.2019.121552

Cited by 29 publications

(12 citation statements)

References 41 publications

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“…This baseline literature study serves as a stepping stone towards the second contribution of this work: an experimental benchmark composed by a selection of modern bio-inspired solvers for finding communities in weighted directed graphs. This class of networks has been far less studied than other graphs despite its straightforward applicability to real-world scenarios, such as the design of network protocols [113], relationships in the control structure of financial networks [13], trade imbalance relationships between importers and exporters [158], or interactions in social network analysis [103]. To this end, we give design rationale on how to adapt search-based heuristic operators to the specific characteristics of this problem, yielding a portfolio of 8 heterogeneous search operators based on diverse design principles (from ad-hoc heuristics to blind movement patterns).…”

Section: Objective and Contributionmentioning

confidence: 99%

Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics

Osaba¹,

Ser

Camacho

et al. 2020

Applied Soft Computing

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Section: Objective and Contributionmentioning

confidence: 99%

Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics

Osaba¹,

Ser

Camacho

et al. 2020

Applied Soft Computing

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“…The idea behind their method is that two people with a mutual friend are more likely to belong to the same community. In trying to address the community detection process, a study in [44] proposed a divisive method, namely local edge centrality (LEC) for community detection, which uses the node dissimilarity and edge betweenness degree. The node dissimilarity in the proposed method is based on the neighbors of the node.…”

Section: B Dynamic Algorithmsmentioning

confidence: 99%

“…Their method is a neighbor-based method, where the joining and merging operation is based on the degree of the neighbor. However, the studies further opined that "In a social network, people in the same group or community usually have common background and interests" ( [44], p.2), which they only considered as the structural properties of the network. In this regard, the process of assigning weight to edges is a core advancement of the study.…”

Section: B Dynamic Algorithmsmentioning

confidence: 99%

A Temporal User Attribute-Based Algorithm to Detect Communities in Online Social Networks

Mahmoudi¹,

Bakar

Sookhak

et al. 2020

IEEE Access

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The world is witnessing the daily emergence of a vast variety of online social networks and community detection problem is a major research area in online social network studies. The existing community detection algorithms are mostly edge-based and are evaluated using the modularity metric benchmarks. However, these algorithms have two inherent limitations. Firstly, they are based on a pure mathematical object which considers the number of connections in each community as the main measures. Consequently, a resolution limit and low accuracy in finding community members in often observed. Whereas, online social networks are dynamic networks and the key players are humans whose main attributes such as lifespan, geo-location, the density of interactions, and user weight, change over time. These attributes tend to influence the formation of user communities in any category of online social network. Secondly, the output structure of existing community detection algorithms is usually provided as a graph and dendrogram. A graph structure, is, however, characterized by a high memory complexity, and subsequently exponential search time complexity. Implementing dendrogram such a complex structure is complicated. To address memory complexity and the accuracy rate of the community detection issues, this paper proposes a new temporal user attribute-based algorithm, namely the recently largest interaction based on the attributes of a typical online social network user. Experimental results show that the proposed algorithm outperforms eight well-known algorithms in this domain.

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“…Although the algorithm that is based on NMF has good interpretability, it usually needs the prior knowledge of the number of communities in the network, but the number of communities is generally unknown. Community detection algorithms that are based on network topology [ 33 , 34 , 35 , 36 , 37 ] have also been proposed recently. Other community detection algorithms were mentioned in [ 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

Zhang

Zhao

et al. 2021

Entropy

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Community detection is of great significance in understanding the structure of the network. Label propagation algorithm (LPA) is a classical and effective method, but it has the problems of randomness and instability. An improved label propagation algorithm named LPA-MNI is proposed in this study by combining the modularity function and node importance with the original LPA. LPA-MNI first identify the initial communities according to the value of modularity. Subsequently, the label propagation is used to cluster the remaining nodes that have not been assigned to initial communities. Meanwhile, node importance is used to improve the node order of label updating and the mechanism of label selecting when multiple labels are contained by the maximum number of nodes. Extensive experiments are performed on twelve real-world networks and eight groups of synthetic networks, and the results show that LPA-MNI has better accuracy, higher modularity, and more reasonable community numbers when compared with other six algorithms. In addition, LPA-MNI is shown to be more robust than the traditional LPA algorithm.

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Communities detection in social network based on local edge centrality

Cited by 29 publications

References 41 publications

Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics

Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics

A Temporal User Attribute-Based Algorithm to Detect Communities in Online Social Networks

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

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