npj Complexity最新文献

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A computational topology-based spatiotemporal analysis technique for honeybee aggregation 基于计算拓扑的蜜蜂聚集时空分析技术

npj Complexity Pub Date : 2024-04-17 DOI: 10.1038/s44260-024-00003-1

Golnar Gharooni-Fard, Morgan Byers, Varad Deshmukh, Elizabeth Bradley, Carissa Mayo, Chad M. Topaz, Orit Peleg

{"title":"A computational topology-based spatiotemporal analysis technique for honeybee aggregation","authors":"Golnar Gharooni-Fard, Morgan Byers, Varad Deshmukh, Elizabeth Bradley, Carissa Mayo, Chad M. Topaz, Orit Peleg","doi":"10.1038/s44260-024-00003-1","DOIUrl":"10.1038/s44260-024-00003-1","url":null,"abstract":"A primary challenge in understanding collective behavior is characterizing the spatiotemporal dynamics of the group. We employ topological data analysis to explore the structure of honeybee aggregations that form during trophallaxis, which is the direct exchange of food among nestmates. From the positions of individual bees, we build topological summaries called CROCKER matrices to track the morphology of the group as a function of scale and time. Each column of a CROCKER matrix records the number of topological features, such as the number of components or holes, that exist in the data for a range of analysis scales, at a given point in time. To detect important changes in the morphology of the group from this information, we first apply dimensionality reduction techniques to these matrices and then use classic clustering and change-point detection algorithms on the resulting scalar data. A test of this methodology on synthetic data from an agent-based model of honeybees and their trophallaxis behavior shows two distinct phases: a dispersed phase that occurs before food is introduced, followed by a food-exchange phase during which aggregations form. We then move to laboratory data, successfully detecting the same two phases across multiple experiments. Interestingly, our method reveals an additional phase change towards the end of the experiments, suggesting the possibility of another dispersed phase that follows the food-exchange phase.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00003-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive link dynamics drive online hate networks and their mainstream influence 自适应链接动态驱动网络仇恨及其主流影响

npj Complexity Pub Date : 2024-04-17 DOI: 10.1038/s44260-024-00002-2

Minzhang Zheng, Richard F. Sear, Lucia Illari, Nicholas J. Restrepo, Neil F. Johnson

引用次数: 0

Phase transitions of civil unrest across countries and time 不同国家和不同时期内乱的阶段性转变

npj Complexity Pub Date : 2024-04-17 DOI: 10.1038/s44260-024-00001-3

Dan Braha

{"title":"Phase transitions of civil unrest across countries and time","authors":"Dan Braha","doi":"10.1038/s44260-024-00001-3","DOIUrl":"10.1038/s44260-024-00001-3","url":null,"abstract":"Phase transitions, characterized by abrupt shifts between macroscopic patterns of organization, are ubiquitous in complex systems. Despite considerable research in the physical and natural sciences, the empirical study of this phenomenon in societal systems is relatively underdeveloped. The goal of this study is to explore whether the dynamics of collective civil unrest can be plausibly characterized as a sequence of recurrent phase shifts, with each phase having measurable and identifiable latent characteristics. Building on previous efforts to characterize civil unrest as a self-organized critical system, we introduce a macro-level statistical model of civil unrest and evaluate its plausibility using a comprehensive dataset of civil unrest events in 170 countries from 1946 to 2017. Our findings demonstrate that the macro-level phase model effectively captures the characteristics of civil unrest data from diverse countries globally and that universal mechanisms may underlie certain aspects of the dynamics of civil unrest. We also introduce a scale to quantify a country’s long-term unrest per unit of time and show that civil unrest events tend to cluster geographically, with the magnitude of civil unrest concentrated in specific regions. Our approach has the potential to identify and measure phase transitions in various collective human phenomena beyond civil unrest, contributing to a better understanding of complex social systems.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00001-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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