Impact of second-order network motif on online social networks.

IF 2.5 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Journal of Supercomputing Pub Date : 2022-01-01 Epub Date: 2021-09-24 DOI:10.1007/s11227-021-04079-7
Sankhamita Sinha, Subhayan Bhattacharya, Sarbani Roy
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引用次数: 1

Abstract

The behaviour of individual users in an online social network is a major contributing factor in determining the outcome of multiple network phenomenon. Group formation, growth of the network, information propagation, and rumour blocking are some of the many network behavioural traits that are influenced by the interaction patterns of the users in the network. Network motifs capture one such interaction pattern between users in online social networks (OSNs). For this work, four second-order (two-edged) network motifs have been considered, namely, message receiving pattern, message broadcasting pattern, message passing pattern, and reciprocal message pattern, to analyse user behaviour in online social networks. This work provides and utilizes a node interaction pattern-finding algorithm to identify the frequency of aforementioned second-order network motifs in six real-life online social networks (Facebook, GPlus, GNU, Twitter, Enron Email, and Wiki-vote). The frequency of network motifs participated in by a node is considered for the relative ranking of all nodes in the online social networks. The highest-rated nodes are considered seeds for information propagation. The performance of using network motifs for ranking nodes as seeds for information propagation is validated using statistical metrics Z-score, concentration, and significance profile and compared with baseline ranking methods in-degree centrality, out-degree centrality, closeness centrality, and PageRank. The comparative study shows the performance of centrality measures to be similar or better than second-order network motifs as seed nodes in information diffusion. The experimental results on finding frequencies and importance of different interaction patterns provide insights on the significance and representation of each such interaction pattern and how it varies from network to network.

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二阶网络母题对在线社交网络的影响。
在线社交网络中个体用户的行为是决定多重网络现象结果的主要因素。群体的形成、网络的成长、信息的传播和谣言的阻断是许多网络行为特征中的一些,这些特征受网络中用户的交互模式的影响。网络主题捕捉了在线社交网络(osn)中用户之间的一种这样的交互模式。在这项工作中,考虑了四个二阶(双刃)网络基序,即消息接收模式、消息广播模式、消息传递模式和互惠消息模式,以分析在线社交网络中的用户行为。这项工作提供并利用了一个节点交互模式查找算法来识别六个现实生活中的在线社交网络(Facebook、GPlus、GNU、Twitter、安然电子邮件和维基投票)中上述二阶网络基序的频率。一个节点参与网络母题的频率被认为是在线社交网络中所有节点的相对排名。评级最高的节点被认为是信息传播的种子。使用网络主题作为信息传播种子对节点排序的性能进行了验证,使用统计指标Z-score、浓度和显著性概况,并与基线排序方法进行了度中心性、度外中心性、接近中心性和PageRank的比较。对比研究表明,中心性测度在信息扩散中的表现与作为种子节点的二阶网络基序相似或优于二阶网络基序。在寻找不同交互模式的频率和重要性方面的实验结果提供了对每种交互模式的意义和表示以及它如何在网络之间变化的见解。
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来源期刊
Journal of Supercomputing
Journal of Supercomputing 工程技术-工程:电子与电气
CiteScore
6.30
自引率
12.10%
发文量
734
审稿时长
13 months
期刊介绍: The Journal of Supercomputing publishes papers on the technology, architecture and systems, algorithms, languages and programs, performance measures and methods, and applications of all aspects of Supercomputing. Tutorial and survey papers are intended for workers and students in the fields associated with and employing advanced computer systems. The journal also publishes letters to the editor, especially in areas relating to policy, succinct statements of paradoxes, intuitively puzzling results, partial results and real needs. Published theoretical and practical papers are advanced, in-depth treatments describing new developments and new ideas. Each includes an introduction summarizing prior, directly pertinent work that is useful for the reader to understand, in order to appreciate the advances being described.
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