npj Complexity最新文献_第2页

Feature-aware ultra-low dimensional reduction of real networks 对真实网络进行特征感知的超低维缩减

npj Complexity Pub Date : 2024-09-02 DOI: 10.1038/s44260-024-00013-z

Robert Jankowski, Pegah Hozhabrierdi, Marián Boguñá, M. Ángeles Serrano

{"title":"Feature-aware ultra-low dimensional reduction of real networks","authors":"Robert Jankowski, Pegah Hozhabrierdi, Marián Boguñá, M. Ángeles Serrano","doi":"10.1038/s44260-024-00013-z","DOIUrl":"10.1038/s44260-024-00013-z","url":null,"abstract":"In existing models and embedding methods of networked systems, node features describing their qualities are usually overlooked in favor of focusing solely on node connectivity. This study introduces FiD-Mercator, a model-based ultra-low dimensional reduction technique that integrates node features with network structure to create D-dimensional maps of complex networks in a hyperbolic space. This embedding method efficiently uses features as an initial condition, guiding the search of nodes’ coordinates toward an optimal solution. The research reveals that downstream task performance improves with the correlation between network connectivity and features, emphasizing the importance of such correlation for enhancing the description and predictability of real networks. Simultaneously, hyperbolic embedding’s ability to reproduce local network properties remains unaffected by the inclusion of features. The findings highlight the necessity for developing network embedding techniques capable of exploiting such correlations to optimize both network structure and feature association jointly in the future.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00013-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117961","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

Upper bounds on overshoot in SIR models with nonlinear incidence 非线性入射的 SIR 模型超调上限

npj Complexity Pub Date : 2024-08-01 DOI: 10.1038/s44260-024-00010-2

Maximilian M. Nguyen

引用次数: 0

Antifragility in complex dynamical systems 复杂动力系统中的反脆弱

npj Complexity Pub Date : 2024-08-01 DOI: 10.1038/s44260-024-00014-y

Cristian Axenie, Oliver López-Corona, Michail A. Makridis, Meisam Akbarzadeh, Matteo Saveriano, Alexandru Stancu, Jeffrey West

{"title":"Antifragility in complex dynamical systems","authors":"Cristian Axenie, Oliver López-Corona, Michail A. Makridis, Meisam Akbarzadeh, Matteo Saveriano, Alexandru Stancu, Jeffrey West","doi":"10.1038/s44260-024-00014-y","DOIUrl":"10.1038/s44260-024-00014-y","url":null,"abstract":"Antifragility characterizes the benefit of a dynamical system derived from the variability in environmental perturbations. Antifragility carries a precise definition that quantifies a system’s output response to input variability. Systems may respond poorly to perturbations (fragile) or benefit from perturbations (antifragile). In this manuscript, we review a range of applications of antifragility theory in technical systems (e.g., traffic control, robotics) and natural systems (e.g., cancer therapy, antibiotics). While there is a broad overlap in methods used to quantify and apply antifragility across disciplines, there is a need for precisely defining the scales at which antifragility operates. Thus, we provide a brief general introduction to the properties of antifragility in applied systems and review relevant literature for both natural and technical systems’ antifragility. We frame this review within three scales common to technical systems: intrinsic (input–output nonlinearity), inherited (extrinsic environmental signals), and induced (feedback control), with associated counterparts in biological systems: ecological (homogeneous systems), evolutionary (heterogeneous systems), and interventional (control). We use the common noun in designing systems that exhibit antifragile behavior across scales and guide the reader along the spectrum of fragility–adaptiveness–resilience–robustness–antifragility, the principles behind it, and its practical implications.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00014-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968458","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

The Ising model celebrates a century of interdisciplinary contributions 伊辛模型跨学科贡献百年庆典

npj Complexity Pub Date : 2024-07-11 DOI: 10.1038/s44260-024-00012-0

Michael W. Macy, Boleslaw K. Szymanski, Janusz A. Hołyst

引用次数: 0

A scalable synergy-first backbone decomposition of higher-order structures in complex systems 复杂系统中高阶结构的可扩展协同效应优先骨干分解

npj Complexity Pub Date : 2024-07-02 DOI: 10.1038/s44260-024-00011-1

Thomas F. Varley

{"title":"A scalable synergy-first backbone decomposition of higher-order structures in complex systems","authors":"Thomas F. Varley","doi":"10.1038/s44260-024-00011-1","DOIUrl":"10.1038/s44260-024-00011-1","url":null,"abstract":"In the last decade, there has been an explosion of interest in the field of multivariate information theory and the study of emergent, higher-order interactions. These “synergistic” dependencies reflect information that is in the “whole” but not any of the “parts.” Arguably the most successful framework for exploring synergies is the partial information decomposition (PID). Despite its considerable power, the PID has a number of limitations that restrict its general applicability. Subsequently, other heuristic measures, such as the O-information, have been introduced, although these measures typically only provide a summary statistic of redundancy/synergy dominance, rather than direct insight into the synergy itself. To address this issue, we present an alternative decomposition that is synergy-first, scales much more gracefully than the PID, and has a straightforward interpretation. We define synergy as that information encoded in the joint state of a set of elements that would be lost following the minimally invasive perturbation on any single element. By generalizing this idea to sets of elements, we construct a totally ordered “backbone” of partial synergy atoms that sweeps the system’s scale. This approach applies to the entropy, the Kullback-Leibler divergence, and by extension, to the total correlation and the single-target mutual information (thus recovering a “backbone” PID). Finally, we show that this approach can be used to decompose higher-order interactions beyond information theory by showing how synergistic combinations of edges in a graph support global integration via communicability. We conclude by discussing how this perspective on synergistic structure can deepen our understanding of part-whole relationships in complex systems.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00011-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500525","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

Affective polarization and dynamics of information spread in online networks 在线网络中的情感极化和信息传播动态

npj Complexity Pub Date : 2024-06-07 DOI: 10.1038/s44260-024-00008-w

Kristina Lerman, Dan Feldman, Zihao He, Ashwin Rao

{"title":"Affective polarization and dynamics of information spread in online networks","authors":"Kristina Lerman, Dan Feldman, Zihao He, Ashwin Rao","doi":"10.1038/s44260-024-00008-w","DOIUrl":"10.1038/s44260-024-00008-w","url":null,"abstract":"Members of different political groups not only disagree about issues but also dislike and distrust each other. While social media can amplify this emotional divide—called affective polarization by political scientists—there is a lack of agreement on its strength and prevalence. We measure affective polarization on social media by quantifying the emotions and toxicity of reply interactions. We demonstrate that, as predicted by affective polarization, interactions between users with same ideology (in-group replies) tend to be positive, while interactions between opposite-ideology users (out-group replies) are characterized by negativity and toxicity. Second, we show that affective polarization generalizes beyond the in-group/out-group dichotomy and can be considered a structural property of social networks. Specifically, we show that emotions vary with network distance between users, with closer interactions eliciting positive emotions and more distant interactions leading to anger, disgust, and toxicity. Finally, we show that similar information exhibits different dynamics when spreading in emotionally polarized groups. These findings are consistent across diverse datasets spanning discussions on topics such as the COVID-19 pandemic and abortion in the US. Our research provides insights into the complex social dynamics of affective polarization in the digital age and its implications for political discourse.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00008-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141287016","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

Epidemic forecast follies 流行病预报谬误

npj Complexity Pub Date : 2024-06-07 DOI: 10.1038/s44260-024-00007-x

P. L. Krapivsky, S. Redner

引用次数: 0

Catalysing cooperation: the power of collective beliefs in structured populations 催化合作：结构化人群中集体信念的力量

npj Complexity Pub Date : 2024-05-29 DOI: 10.1038/s44260-024-00005-z

Małgorzata Fic, Chaitanya S. Gokhale

{"title":"Catalysing cooperation: the power of collective beliefs in structured populations","authors":"Małgorzata Fic, Chaitanya S. Gokhale","doi":"10.1038/s44260-024-00005-z","DOIUrl":"10.1038/s44260-024-00005-z","url":null,"abstract":"Collective beliefs can catalyse cooperation in a population of selfish individuals. We study this transformative power of collective beliefs, an effect that intriguingly persists even when beliefs lack moralising components. Besides the process itself, we consider the structure of human populations explicitly. We incorporate the intricate structure of human populations into our model, acknowledging the bias brought by social and cultural identities in interaction networks. Hence, we develop our model by assuming a heterogeneous group size and structured population. We recognise that beliefs, typically complex story systems, might not spontaneously emerge in society, resulting in different spreading rates for actions and beliefs within populations. As the degree of connectedness can vary among individuals perpetuating a belief, we examine the speed of trust build-up in networks with different connection densities. We then scrutinise the timing, speed and dynamics of trust and belief spread across specific network structures, including random Erdös-Rényi networks, scale-free Barabási-Albert networks, and small-world Newman-Watts-Strogatz networks. By comparing these characteristics across various network topologies, we disentangle the effects of structure, group size diversity, and evolutionary dynamics on the evolution of trust and belief.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00005-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141165068","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

Diverse misinformation: impacts of human biases on detection of deepfakes on networks 多样化的错误信息：人类偏见对网络深度伪造检测的影响

npj Complexity Pub Date : 2024-05-18 DOI: 10.1038/s44260-024-00006-y

Juniper Lovato, Jonathan St-Onge, Randall Harp, Gabriela Salazar Lopez, Sean P. Rogers, Ijaz Ul Haq, Laurent Hébert-Dufresne, Jeremiah Onaolapo

{"title":"Diverse misinformation: impacts of human biases on detection of deepfakes on networks","authors":"Juniper Lovato, Jonathan St-Onge, Randall Harp, Gabriela Salazar Lopez, Sean P. Rogers, Ijaz Ul Haq, Laurent Hébert-Dufresne, Jeremiah Onaolapo","doi":"10.1038/s44260-024-00006-y","DOIUrl":"10.1038/s44260-024-00006-y","url":null,"abstract":"Social media platforms often assume that users can self-correct against misinformation. However, social media users are not equally susceptible to all misinformation as their biases influence what types of misinformation might thrive and who might be at risk. We call “diverse misinformation” the complex relationships between human biases and demographics represented in misinformation. To investigate how users’ biases impact their susceptibility and their ability to correct each other, we analyze classification of deepfakes as a type of diverse misinformation. We chose deepfakes as a case study for three reasons: (1) their classification as misinformation is more objective; (2) we can control the demographics of the personas presented; (3) deepfakes are a real-world concern with associated harms that must be better understood. Our paper presents an observational survey (N = 2016) where participants are exposed to videos and asked questions about their attributes, not knowing some might be deepfakes. Our analysis investigates the extent to which different users are duped and which perceived demographics of deepfake personas tend to mislead. We find that accuracy varies by demographics, and participants are generally better at classifying videos that match them. We extrapolate from these results to understand the potential population-level impacts of these biases using a mathematical model of the interplay between diverse misinformation and crowd correction. Our model suggests that diverse contacts might provide “herd correction” where friends can protect each other. Altogether, human biases and the attributes of misinformation matter greatly, but having a diverse social group may help reduce susceptibility to misinformation.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00006-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141069197","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

The path of complexity 复杂性之路

npj Complexity Pub Date : 2024-04-17 DOI: 10.1038/s44260-024-00004-0

Laurent Hébert-Dufresne, Antoine Allard, Joshua Garland, Elizabeth A. Hobson, Luis Zaman

引用次数: 0