ArticleEntropy (Basel, Switzerland)2020
Identifying Influencers in Social Networks.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Application of Grey Correlation Model to Key Spreader Identification in Multiplex Networks.Entropy (Basel, Switzerland) · 2026Article
- Location based bursty event detection and information dissemination using influencers in Twitter.Scientific reports · 2026Article
- High-Quality Representation Learning Approach to Spatio-Temporal Traffic Speed Data withEntropy (Basel, Switzerland) · 2026Article
- Persistent Homology Combined with Machine Learning for Social Network Activity Analysis.Entropy (Basel, Switzerland) · 2024Article
- Mapping the Mpox discourse: A network and sentiment analysis.Exploratory research in clinical and social pharmacy · 2024Article
- Construction of knowledge sharing network indicator system for medication therapy management service training teams based on social network analysis.BMC medical education · 2024Article
- Identifying influential spreaders by gravity model considering multi-characteristics of nodes.Scientific reports · 2022Article
- Social Influence Maximization in Hypergraphs.Entropy (Basel, Switzerland) · 2021Article
- Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo.International journal of environmental research and public health · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.