An Introduction to Community Detection in Multi-layered Social Network

Bródka, Piotr; Filipowski, T.; Kazienko, Przemysław

doi:10.1007/978-3-642-35879-1_23

Cited by 21 publications

(12 citation statements)

References 11 publications

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“…In [26,27], a community detection algorithm based on multilevel network clustering was presented, where the original multilevel networks were first merged into a single-level network under certain strategy before the community detection method for the single-layer network was implemented. In [28][29][30][31][32], a community detection method based on consensus clustering was described. e community detection algorithm for the singlelayer network was first implemented on each layer of network.…”

Section: Multilevel Network Community Detectionmentioning

confidence: 99%

Consumers Team Detection Model Based on Trust for Multi-Level

Armoogum

et al. 2019

Mobile Information Systems

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Due to rapid advances in technology, social networks have become important platforms for daily communication, product marketing, and information dissemination. Targeted delivery of social network advertisement can considerably improve the efficacy of the advertisement and maximize the profits from it. In this context, managing the specific audience of a social network advertisement and achieving targeted advertisement delivery have been the ultimate goals of the social network advertising sector. Identifying user groups with similar properties is critical to increasing targeted sales. When both the scale of mobile social network and the coplexity of social network user behaviors grow, similar groups are hidden in user behaviors. In order to analyze community structure with user trust relationship more appropriately in the large-scale multilevel social network environment, a novel local community detection model E-MLCD is proposed in this paper. It is jointly based on the multilevel properties and the strength of similarity of multilevel social interaction among communities. By studying three real-world multilevel social networks and specific QQ Zone marketing data, the model defines a new metric of community trust based on similarity. Comparison between other state-of-the-art detection methods demonstrate E-MLCD’s ability to detect communities more effectively.

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Section: Multilevel Network Community Detectionmentioning

confidence: 99%

Consumers Team Detection Model Based on Trust for Multi-Level

Armoogum

et al. 2019

Mobile Information Systems

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“…A Multi-layered Social Network is defined as a tuple V, E, L where: V is a non-empty set of nodes, E is a set of edges where each edge belongs to exactly one layer and L is a set of layers [5]. Each layer corresponds to one type of relationship between users [7].…”

Section: State-of-the-art Extended Models Of Social Networkmentioning

confidence: 99%

Mobile Social Networks for Live Meetings

Wrzeszcz

Kitowski

2012

csci

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“…This method does not consider that the meaning of the nodes in each layer may be different [19]. Multilayer combination analysis directly detects the community in a multilayer network [20]. The cross-layer edge clustering coefficient (CLECC) used for multilayer network community detection is proposed based on the edge cluster coefficient, such as tensor decomposition [21,22], the method [23][24][25] based on modularity Qm.…”

Section: Introductionmentioning

confidence: 99%

A Feasible Community Detection Algorithm for Multilayer Networks

Chen

Jiang

et al. 2020

Symmetry

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As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilayer networks can identify the underlying structures that contain various intralayer and interlayer relationships, which is of significance and remains a challenge. In this paper, aiming at the instability of the label propagation algorithm (LPA), an improved label propagation algorithm based on the SH-index (SH-LPA) is proposed. By analyzing the characteristics and deficiencies of the H-index, the SH-index is presented as an index to evaluate the importance of nodes, and the stability of the SH-LPA algorithm is verified by a series of experiments. Afterward, considering the deficiency of the existing multilayer network aggregation model, we propose an improved multilayer network aggregation model that merges two networks into a weighted single-layer network. Finally, considering the influence of the SH-index and the weight of the edge of the weighted network, a community detection algorithm (MSH-LPA) suitable for multilayer networks is exhibited in terms of the SH-LPA algorithm, and the superiority of the mentioned algorithm is verified by experimental analysis.

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An Introduction to Community Detection in Multi-layered Social Network

Cited by 21 publications

References 11 publications

Consumers Team Detection Model Based on Trust for Multi-Level

Consumers Team Detection Model Based on Trust for Multi-Level

Mobile Social Networks for Live Meetings

A Feasible Community Detection Algorithm for Multilayer Networks

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