Trophic analysis of a historical network reveals temporal information

被引:3
作者
Shuaib, Choudhry [1 ]
Syed, Mairaj [2 ]
Halawi, Danny [3 ]
Saquib, Nazmus [4 ]
机构
[1] Univ Warwick, Dept Comp Sci, Warwick, England
[2] Univ Calif Davis, Dept Religious Studies, Davis, CA 95616 USA
[3] Univ Calif Berkeley, Dept Comp Sci, Berkeley, CA 94720 USA
[4] Tero Labs, Santa Clara, CA USA
基金
英国工程与自然科学研究理事会;
关键词
Social network; Historical network; Temporal network; Trophic analysis; HIERARCHY;
D O I
10.1007/s41109-022-00469-9
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Trophic analysis exposes the underlying hierarchies present in large complex systems. This allows one to use data to diagnose the sources, propagation paths, and basins of influence of shocks or information among variables or agents, which may be utilised to analyse dynamics in social, economic and historical data sets. Often, the analysis of static networks provides an aggregated picture of a dynamical process and explicit temporal information is typically missing or incomplete. Yet, for many networks, particularly historical ones, temporal information is often implicit, for example in the direction of edges in a network. In this paper, we show that the application of trophic analysis allows one to use the network structure to infer temporal information. We demonstrate this on a sociohistorical network derived from the study of hadith, which are narratives about the Prophet Muhammad's actions and sayings that cite the people that transmitted the narratives from one generation to the next before they were systematically written down. We corroborate the results of the trophic analysis with a partially specified time labelling of a subset of the transmitters. The results correlate in a manner consistent with an observed history of information transmission flowing through the network. Thus, we show that one may reconstruct a temporal structure for a complex network in which information diffuses from one agent to another via social links and thus allows for the reconstruction of an event based temporal network from an aggregated static snapshot. Our paper demonstrates the utility of trophic analysis in revealing novel information from hierarchical structure, thus showing its potential for probing complex systems, particularly those with an inherent asymmetry.
引用
收藏
页数:18
相关论文
共 50 条
[1]   Learning to Identify Narrators in Classical Arabic Texts [J].
Alkaoud, Mohamed ;
Syed, Mairaj .
AI IN COMPUTATIONAL LINGUISTICS, 2021, 189 :335-342
[2]  
[Anonymous], 1994, Fractal cities: a geometry of form and function
[3]   Measuring the Upstreamness of Production and Trade Flows [J].
Antras, Pol ;
Chor, Davin ;
Fally, Thibault ;
Hillberry, Russell .
AMERICAN ECONOMIC REVIEW, 2012, 102 (03) :412-416
[4]   Structure and dynamical behavior of non-normal networks [J].
Asllani, Malbor ;
Lambiotte, Renaud ;
Carletti, Timoteo .
SCIENCE ADVANCES, 2018, 4 (12)
[5]   Non-normality Improves Information Transmission Performance of Network Systems [J].
Baggio, Giacomo ;
Zampieri, Sandro .
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2021, 8 (04) :1846-1858
[6]   Efficient communication over complex dynamical networks: The role of matrix non-normality [J].
Baggio, Giacomo ;
Rutten, Virginia ;
Hennequin, Guillaume ;
Zampieri, Sandro .
SCIENCE ADVANCES, 2020, 6 (22)
[7]  
Barrat A., 2008, Dynamical Processes on Complex Networks
[8]  
Brown JAC., 2009, HADITH MUHAMMAD S LE
[9]   Combining hierarchy and energy for drawing directed graphs [J].
Carmel, L ;
Harel, D ;
Koren, Y .
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2004, 10 (01) :46-57
[10]   Transitive reduction of citation networks [J].
Clough, James R. ;
Gollings, Jamie ;
Loach, Tamar V. ;
Evans, Tim S. .
JOURNAL OF COMPLEX NETWORKS, 2015, 3 (02) :189-203