Local community detection in multilayer networks

Interdonato, Roberto; Tagarelli, Andrea; Ienco, Dino; Sallaberry, Arnaud; Poncelet, Pascal

doi:10.1007/s10618-017-0525-y

Cited by 51 publications

(28 citation statements)

References 27 publications

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“…We executed some community-detection algorithms across the Twitter event networks, Noordin terrorist relationship networks, student-cooperation social networks and global terrorism networks. These algorithms are BGLL for multiplex networks (BGLLMN) 61 , 62 , bridge detection (BD) 63 ,tensor decomposition for multiplex networks (TD) 64 , Modularity-driven Ensemble-Based Community Detection (M-EMCD) 65 , Multidimensional Label Propagation Algorithm (MDLPA) 66 , Multilayer Local Community Detection (ML-LCD) 67 and our modularity function for multiplex networks (see Supplementary Note 2 ). Figure 3 shows the results of this quantitative comparison (see Supplementary Note 3 ) on three of the tested networks and indicates that the modularity function for multiplex networks results in higher-quality communities than do the other tested methods (see Supplementary Note 3 ).…”

Section: Resultsmentioning

confidence: 99%

Null Model and Community Structure in Multiplex Networks

Zhai

Zhou

Fei

et al. 2018

Sci Rep

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The multiple relationships among objects in complex systems can be described well by multiplex networks, which contain rich information of the connections between objects. The null model of networks, which can be used to quantify the specific nature of a network, is a powerful tool for analysing the structural characteristics of complex systems. However, the null model for multiplex networks remains largely unexplored. In this paper, we propose a null model for multiplex networks based on the node redundancy degree, which is a natural measure for describing the multiple relationships in multiplex networks. Based on this model, we define the modularity of multiplex networks to study the community structures in multiplex networks and demonstrate our theory in practice through community detection in four real-world networks. The results show that our model can reveal the community structures in multiplex networks and indicate that our null model is a useful approach for providing new insights into the specific nature of multiplex networks, which are difficult to quantify.

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Section: Resultsmentioning

confidence: 99%

Null Model and Community Structure in Multiplex Networks

Zhai

Zhou

Fei

et al. 2018

Sci Rep

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“…In addition to the above-mentioned directions, quite a part of algorithms focus on overlapping community detection (Liu et al 2018) and local community detection (Interdonato et al 2017;Jeub et al 2015;Li et al 2019). On the one hand, The complexity of ABACUS depends on the complexity of the employed monolayer algorithms, e.g., O(n) from LPA (Raghavan et al 2007) and with total complexity of The complexity of MLMaOP depends on an uncertain convergence process, thereby marked with "-".…”

Section: Discussionmentioning

confidence: 99%

A survey of community detection methods in multilayer networks

Huang

Chen

Ren

et al. 2020

Data Min Knowl Disc

110

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Community detection is one of the most popular researches in a variety of complex systems, ranging from biology to sociology. In recent years, there’s an increasing focus on the rapid development of more complicated networks, namely multilayer networks. Communities in a single-layer network are groups of nodes that are more strongly connected among themselves than the others, while in multilayer networks, a group of well-connected nodes are shared in multiple layers. Most traditional algorithms can rarely perform well on a multilayer network without modifications. Thus, in this paper, we offer overall comparisons of existing works and analyze several representative algorithms, providing a comprehensive understanding of community detection methods in multilayer networks. The comparison results indicate that the promoting of algorithm efficiency and the extending for general multilayer networks are also expected in the forthcoming studies.

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“…If a path between any two nodes in the multilayer network can be found, then the network is connected; otherwise, it is not connected. However, different types of network connectivity may occur if the edges in different network layers are considered [29].…”

Section: ) Indirect Influence Of Multi-layer Network Nodesmentioning

confidence: 99%

Multi-Layer Network Local Community Detection Based on Influence Relation

Lian

et al. 2019

IEEE Access

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In recent years, the discovery of local communities in multi-layer networks has become an active research field of complex systems. Such as communication, social networking, sensor network the rapid development of new technologies, the amount of data generated by increased, all want to obtain the network information difficulty is big, and the network, community, mutual influence between nodes, greatly enhance the complexity of network and make local found existing multilayer network method is unable to get more accurate test results. In this paper, based on the homogeneity drive of multi-layer network and the influence relation index of multi-layer path length measurement, a local community detection model based on the influence relation of the multi-layer network is proposed by combining the direct influence relation and indirect influence relation of the network (IMLC). Compared with six real multi-layer network data sets, the algorithm has better robustness in many most advanced multi-layer methods: GL, PMM, and ML-LCD.

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Local community detection in multilayer networks

Cited by 51 publications

References 27 publications

Null Model and Community Structure in Multiplex Networks

Null Model and Community Structure in Multiplex Networks

A survey of community detection methods in multilayer networks

Multi-Layer Network Local Community Detection Based on Influence Relation

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