Local community detection for multi-layer mobile network based on the trust relation

Li, Xiaoming; Tian, Qiang; Tang, Minghu; Chen, Xue; Yang, Xiaoxian

doi:10.1007/s11276-019-01938-3

Cited by 9 publications

(8 citation statements)

References 29 publications

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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

111

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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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Section: Discussionmentioning

confidence: 99%

A survey of community detection methods in multilayer networks

Huang

Chen

Ren

et al. 2020

Data Min Knowl Disc

111

View full text Add to dashboard Cite

show abstract

“…Ding et al [27] established a trust evaluation model between users by integrating structural similarity and neighbor similarity and then obtained overlapping communities of single-layer networks by using coarse-grained K-medoids. In contrast, Li et al [28] established a trust model between users by analyzing the homogeneity between nodes and the shortest path and proposed a multilayer network local community discovery algorithm based on trust relationships. However, in the process of establishing a trust model, these algorithms essentially only use the structural features of the network, ignoring the social attributes of the network.…”

Section: Related Workmentioning

confidence: 99%

Multilayer Social Network Overlapping Community Detection Algorithm Based on Trust Relationship

Jia

Liu

et al. 2021

Wireless Communications and Mobile Computing

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Aiming at the problem of the lack of user social attribute characteristics in the process of dividing overlapping communities in multilayer social networks, in this paper, we propose a multilayer social network overlapping community detection algorithm based on trust relationship. By combining structural trust and social attribute trust, we transform a complex multilayer social network into a single-layer trust network. We obtain the community structure according to the community discovery algorithm based on trust value and merge communities with higher overlap. The experimental comparison and analysis are carried out on the synthetic network and the real network, respectively. The experimental results show that the proposed algorithm has higher harmonic mean and modularity than other algorithms of the same type.

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“…Detecting community within a multilevel network has drawn a lot of attention in recent years [18][19][20][21]. Due to the considerable complexity of the real world, the single-level network is no longer able to describe the community very effectively.…”

Section: Multilevel Network Community Detectionmentioning

confidence: 99%

“…is framework enables us to study the community structure of as many slice networks as we want. In [18], Domenico revealed that any complex system can be represented by a multilevel network. For example, organism genes and the interaction between them can be represented by 7 network layers.…”

Section: Multilevel Network Community Detectionmentioning

confidence: 99%

Consumers Team Detection Model Based on Trust for Multi-Level

Armoogum

et al. 2019

Mobile Information Systems

Self Cite

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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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Local community detection for multi-layer mobile network based on the trust relation

Cited by 9 publications

References 29 publications

A survey of community detection methods in multilayer networks

A survey of community detection methods in multilayer networks

Multilayer Social Network Overlapping Community Detection Algorithm Based on Trust Relationship

Consumers Team Detection Model Based on Trust for Multi-Level

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