Competition-Based Benchmarking of Influence Ranking Methods in Social Networks

Author:

Topîrceanu Alexandru1ORCID

Affiliation:

1. Department of Computer and Information Technology, Politehnica University Timisoara, Timisoara, Romania

Abstract

The development of new methods to identify influential spreaders in complex networks has been a significant challenge in network science over the last decade. Practical significance spans from graph theory to interdisciplinary fields like biology, sociology, economics, and marketing. Despite rich literature in this direction, we find small notable effort to consistently compare and rank existing centralities considering both the topology and the opinion diffusion model, as well as considering the context of simultaneous spreading. To this end, our study introduces a new benchmarking framework targeting the scenario of competitive opinion diffusion; our method differs from classic SIR epidemic diffusion, by employing competition-based spreading supported by the realistic tolerance-based diffusion model. We review a wide range of state-of-the-art node ranking methods and apply our novel method on large synthetic and real-world datasets. Simulations show that our methodology offers much higher quantitative differentiation between ranking methods on the same dataset and notably high granularity for a ranking method over different datasets. We are able to pinpoint—with consistency—which influence the ranking method performs better against the other one, on a given complex network topology. We consider that our framework can offer a forward leap when analysing diffusion characterized by real-time competition between agents. These results can greatly benefit the tackling of social unrest, rumour spreading, political manipulation, and other vital and challenging applications in social network analysis.

Funder

Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3