Detection of strong attractors in social media networks.

Q1 Mathematics

Computational Social Networks Pub Date : 2016-01-01 Epub Date: 2016-12-07 DOI:10.1186/s40649-016-0036-9

Ziyaad Qasem, Marc Jansen, Tobias Hecking, H Ulrich Hoppe

{"title":"Detection of strong attractors in social media networks.","authors":"Ziyaad Qasem, Marc Jansen, Tobias Hecking, H Ulrich Hoppe","doi":"10.1186/s40649-016-0036-9","DOIUrl":null,"url":null,"abstract":"Background: Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing.Methods: The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time.Results: We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs.Conclusions: Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"11"},"PeriodicalIF":0.0000,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-016-0036-9","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Social Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40649-016-0036-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2016/12/7 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}

引用次数: 6

Abstract

Background: Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing.

Methods: The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time.

Results: We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs.

Conclusions: Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures.

Abstract Image

查看原文本刊更多论文

社交媒体网络中强吸引子的检测。

背景:在Twitter或Facebook等社交媒体中发现有影响力的行动者，对于提高教育和营销等许多领域的工作和服务的质量和效率起着重要作用。方法:这里描述的工作旨在引入一种新的方法，通过吸引新的活跃成员进入网络社区的力量来表征行动者的影响。我们提出了一个演员的影响力模型，该模型基于演员的吸引力，即他或她随着时间的推移与之建立关系的其他新演员的数量。结果:我们已经使用这个概念和影响力的措施，以确定最优的种子在影响最大化的模拟使用两个经验收集的社会网络的底层图。结论:我们在数据集上的实证结果表明，与其他影响度量相比，我们的度量作为定义吸引子的有用度量脱颖而出。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.