An Efficient Critical Incident Propagation Model for Social Networks Based on Trust Factor

Li, Xiaoming; Yuan, Limengzi; Liu, Chaochao; Yu, Wei; Xue, Chen; Xu, Guangquan

doi:10.1007/978-3-030-00916-8_39

Cited by 1 publication

(1 citation statement)

References 13 publications

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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%

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%