TriBeC: identifying influential users on social networks with upstream and downstream network centrality

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS

International Journal of General Systems Pub Date : 2023-04-03 DOI:10.1080/03081079.2023.2194642

Somya Jain, Adwitiya Sinha

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引用次数: 2

Abstract

The complex heterogeneous nature of social networks generates colossal user data, hence requiring exhaustive efforts to accelerate the propagation of information. This necessitates the identification of central nodes that are considered substantial for information spread and control. Our research proposes a novel centrality metric, TriBeC to identify the significant nodes in online social networks by utilizing the impact of weighted betweenness extended with network quartiles. The proposed approach introduces a user data-driven centrality measure for the discovery of influential nodes in online social networks. This is based on locating the median with the information flowing upstream and downstream, thereby considering the impact of border nodes lying farthest in the network circumference. Experimental outcomes on Twitter, Facebook, BlogCatalog, Scale-free and Random networks show the outperforming results of topmost 1% TriBeC central nodes over existing counterparts in terms of the percentage of the network being infested with information over time.

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TriBeC：识别具有上下游网络中心性的社交网络上有影响力的用户

社交网络的复杂异构性产生了巨大的用户数据，因此需要竭尽全力加速信息的传播。这就需要识别被认为对信息传播和控制至关重要的中心节点。我们的研究提出了一种新的中心性度量TriBeC，通过利用网络四分位数扩展的加权介数的影响来识别在线社交网络中的重要节点。所提出的方法引入了一种用户数据驱动的中心性度量，用于发现在线社交网络中有影响力的节点。这是基于在信息上下游流动的情况下定位中值，从而考虑网络圆周上最远的边界节点的影响。在Twitter、Facebook、BlogCatalog、Scale free和Random网络上的实验结果显示，就网络随时间推移被信息侵扰的百分比而言，排名前1%的TriBeC中心节点的性能优于现有节点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of General Systems 工程技术-计算机：理论方法

CiteScore

4.10

自引率

20.00%

发文量

审稿时长

6 months

期刊介绍： International Journal of General Systems is a periodical devoted primarily to the publication of original research contributions to system science, basic as well as applied. However, relevant survey articles, invited book reviews, bibliographies, and letters to the editor are also published. The principal aim of the journal is to promote original systems ideas (concepts, principles, methods, theoretical or experimental results, etc.) that are broadly applicable to various kinds of systems. The term “general system” in the name of the journal is intended to indicate this aim–the orientation to systems ideas that have a general applicability. Typical subject areas covered by the journal include: uncertainty and randomness; fuzziness and imprecision; information; complexity; inductive and deductive reasoning about systems; learning; systems analysis and design; and theoretical as well as experimental knowledge regarding various categories of systems. Submitted research must be well presented and must clearly state the contribution and novelty. Manuscripts dealing with particular kinds of systems which lack general applicability across a broad range of systems should be sent to journals specializing in the respective topics.