Hybrid modeling approach for contextualized community detection in multilayer social network: emergency management case study

Rebhi, Wala; Yahia, Nesrine Ben; Saoud, Narjès Bellamine Ben

doi:10.1016/j.procs.2017.08.124

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…After reviewing a large amount of literature, starting from the whole process of community emergencies for the four stages of emergency prevention, emergency preparedness, emergency response and emergency recovery [31,32], six dimensions (secondary indicators) of infrastructure, community organization, risk and hidden danger management, emergency material support, emergency force construction, and emergency literacy were preliminarily screened. By visiting and investigating community managers, community residents, merchants, and other personnel, combined with the actual situation of the community and the external environmental factors (community service capacity, community infrastructure construction, community safety culture construction, community management ability, etc.)…”

Section: Construction Of Evaluation Index System For Community Emerge...mentioning

confidence: 99%

An Evaluation Approach of Community Emergency Management Ability Based on Cone-ANP

Wang

Feng

Deng

et al. 2023

IJERPH

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In order to improve the emergency management ability of urban communities in response to emergencies and reduce the losses caused by emergencies, based on the method of Cone-Analytic Network Process (Cone-ANP), a whole-process community emergency management ability evaluation method was proposed. Using around 25 evaluation indexes from six dimensions, namely infrastructure resilience, community organization resilience, risk management, emergency material support, emergency force building, and emergency literacy, this method established the dominant relationship of each index by the analysis of the cone network structure. It determined the community safety culture construction as the cone-top element, and obtained the limit weight vector of all the evaluation indexes by expert evaluation. The membership degree of each index was calculated to quantify the evaluation results of community emergency management ability. The results could provide a guidance and reference basis for community emergency management.

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Section: Construction Of Evaluation Index System For Community Emerge...mentioning

confidence: 99%

An Evaluation Approach of Community Emergency Management Ability Based on Cone-ANP

Wang

Feng

Deng

et al. 2023

IJERPH

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“…), and Neuroticism (indicates a tendency to experience unpleasant emotions easily.) [41]. As for emotional state, it includes eight basic learning emotions: interest, engagement, confusion, frustration, boredom, hopefulness, satisfaction and disappointment, among which the last two emotions are prospect based.…”

Section: Lifelong and Multirelational Community Detection Algorithmmentioning

confidence: 99%

Lifelong and multirelational community detection to support social and collaborative e‐learning

Rebhi

Yahia

Bellamine

2022

Comp Applic In Engineering

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Learners' community choice has a crucial role in e‐learning effectiveness. Indeed, individual and structural factors (i.e., learners pre‐existing profiles and networks of interactions) significantly affect how learners develop a collaborative e‐learning environment. In this context, social network analysis, singularly community detection has been a good approach to improve collaborative environment through discovering pertinent learners' communities and new effective relations. However, with social media emergence, many real‐world networks, such as learners' networks, evolve the connections in multiple layers, where each layer represents a different type of relationship. This, combined with their continuous evolution over time, has brought new challenges to the field of community detection. Thus, this paper proposes a new configurable algorithm for detecting collaborative and lifelong communities within dynamic multirelational social learners' networks. To do so, this algorithm is based on a graph model to represent these different interactions as well as different learners' profiles and characteristics. Moreover, by using particle swarm optimization, it aims to optimize a configurable combined metric to detect the most relevant community appropriate to a given situation. Finally, it considers the temporal dimension to find the final lifelong community. By the end of this paper, experimental results using synthetic networks prove that the proposed algorithm achieves better results compared with other community detection algorithms. Therewith, experiments on a real e‐learning network show this algorithm's role in improving collaboration within learners' network.

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Mining Latent Features of Knowledge Graphs for Predicting Missing Relations

Weller

Dillig

Acosta

et al. 2020

Lecture Notes in Computer Science

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Hybrid modeling approach for contextualized community detection in multilayer social network: emergency management case study

Cited by 6 publications

References 18 publications

An Evaluation Approach of Community Emergency Management Ability Based on Cone-ANP

An Evaluation Approach of Community Emergency Management Ability Based on Cone-ANP

Lifelong and multirelational community detection to support social and collaborative e‐learning

Mining Latent Features of Knowledge Graphs for Predicting Missing Relations

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