Journal of Physics Complexity最新文献

{"title":"Group polarization, influence, and domination in online interaction networks: A case study of the 2022 Brazilian elections","authors":"Ruben Interian, Francisco Aparecido Rodrigues","doi":"10.1088/2632-072x/acf6a4","DOIUrl":"https://doi.org/10.1088/2632-072x/acf6a4","url":null,"abstract":"The erosion of social cohesion and polarization is one of the topmost societal risks. In this work, we investigated the evolution of polarization, influence, and domination in online interaction networks using a large Twitter dataset collected before and during the 2022 Brazilian elections. From a theoretical perspective, we develop a methodology called d-modularity that allows discovering the contribution of specific groups to network polarization using the well-known modularity measure. While the overall network modularity (somewhat unexpectedly) decreased, the proposed group-oriented approach reveals that the contribution of the right-leaning community to this modularity increased, remaining very high during the analyzed period. Our methodology is general enough to be used in any situation when the contribution of specific groups to overall network modularity and polarization is needed to investigate. Moreover, using the concept of partial domination, we are able to compare the reach of sets of influential profiles from different groups and their ability to accomplish coordinated communication inside their groups and across segments of the entire network. We show that in the whole network, the left-leaning high-influential information spreaders dominated, reaching a substantial fraction of users with fewer spreaders. However, when comparing domination inside the groups, the results are inverse. Right-leaning spreaders dominate their communities using few nodes, showing as the most capable of accomplishing coordinated communication. The results bring evidence of extreme isolation and the ease of accomplishing coordinated communication that characterized right-leaning communities during the 2022 Brazilian elections, which likely influenced the subsequent coup events in Brasilia.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135098462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crossover phenomenon in adversarial attacks on voter model","authors":"Shogo Mizutaka","doi":"10.1088/2632-072x/acf90b","DOIUrl":"https://doi.org/10.1088/2632-072x/acf90b","url":null,"abstract":"Abstract A recent study (Chiyomaru and Takemoto 2022 Phys. Rev. E 106 014301) considered adversarial attacks conducted to distort voter model dynamics in networks. This method intervenes in the interaction patterns of individuals and induces them to be in a target opinion state through a small perturbation ε . In this study, we investigate adversarial attacks on voter dynamics in random networks of finite size n . The exit probability P +1 to reach the target absorbing state and the mean time τ n to reach consensus are analyzed in the mean-field approximation. Given ε > 0, the exit probability P +1 converges asymptotically to unity as n increases. The mean time τ n to reach consensus scales as <?CDATA $(ln epsilon n)/epsilon$?> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" overflow=\"scroll\"> <mml:mrow> <mml:mo stretchy=\"false\">(</mml:mo> <mml:mi>ln</mml:mi> <mml:mi>ϵ</mml:mi> <mml:mi>n</mml:mi> <mml:mo stretchy=\"false\">)</mml:mo> <mml:mo>/</mml:mo> <mml:mi>ϵ</mml:mi> </mml:mrow> </mml:math> for homogeneous networks with a large finite n . By contrast, it scales as <?CDATA $(ln (epsilonmu_1^2n/mu_2))/epsilon$?> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" overflow=\"scroll\"> <mml:mo stretchy=\"false\">(</mml:mo> <mml:mo form=\"prefix\">ln</mml:mo> <mml:mo stretchy=\"false\">(</mml:mo> <mml:mi>ϵ</mml:mi> <mml:msubsup> <mml:mi>μ</mml:mi> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:msubsup> <mml:mi>n</mml:mi> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:msub> <mml:mi>μ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo stretchy=\"false\">)</mml:mo> <mml:mo stretchy=\"false\">)</mml:mo> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mi>ϵ</mml:mi> </mml:math> for heterogeneous networks with a large finite n , where µ 1 and µ 2 represent the first and second moments of the degree distribution, respectively. Moreover, we observe the crossover phenomenon of τ n from a linear scale to a logarithmic scale and find <?CDATA $n_{mathrm{co}}sim epsilon^{-1/alpha}$?> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" overflow=\"scroll\"> <mml:msub> <mml:mi>n</mml:mi> <mml:mrow> <mml:mrow> <mml:mi mathvariant=\"normal\">c</mml:mi> <mml:mi mathvariant=\"normal\">o</mml:mi> </mml:mrow> </mml:mrow> </mml:msub> <mml:mo>∼</mml:mo> <mml:msup> <mml:mi>ϵ</mml:mi> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mi>α</mml:mi> </mml:mrow> </mml:msup> </mml:math> above which the state of all nodes becomes the target state in logarithmic time. Here, α = 1 for homogeneous networks and <?CDATA $alpha = (gamma-1)/2$?> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" overflow=\"scroll\"> <mml:mi>α</mml:mi> <mml:mo>=</mml:mo> <mml:mo stretchy=\"false\">(</mml:mo> <mml:mi>γ</mml:mi> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy=\"false\">)</mml:mo> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:math> for scale-free networks with a degree exponent <?CDATA $2ltgammalt3$?> <mml:math xmlns:mml=\"ht","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135255215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning capacity and function of stochastic reaction networks","authors":"A. Ramezanpour, A. Mashaghi","doi":"10.1088/2632-072X/acf264","DOIUrl":"https://doi.org/10.1088/2632-072X/acf264","url":null,"abstract":"Biochemical reaction networks are expected to encode an efficient representation of the function of cells in a variable environment. It is thus important to see how these networks do learn and implement such representations. The first step in this direction is to characterize the function and learning capabilities of basic artificial reaction networks. In this study, we consider multilayer networks of reversible reactions that connect two layers of signal and response species through an intermediate layer of hidden species. We introduce a stochastic learning algorithm that updates the reaction rates based on the correlation values between reaction products and responses. Our findings indicate that the function of networks with random reaction rates, as well as their learning capacity for random signal-response activities, are critically determined by the number of reactants and reaction products. Moreover, the stored patterns exhibit different levels of robustness and qualities as the reaction rates deviate from their optimal values in a stochastic model of defect evolution. These findings can help suggest network modules that are better suited to specific functions, such as amplifiers or dampeners, or to the learning of biologically relevant signal-response activities.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48587367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reasoning of economic complexity based on simulated general equilibrium international trade model","authors":"Yumin Hu, Zhongchen Fan, Justin Yifu Lin, Mingzhi Xu","doi":"10.1088/2632-072X/ace39e","DOIUrl":"https://doi.org/10.1088/2632-072X/ace39e","url":null,"abstract":"By simulating a multi-country general equilibrium international trade model, we investigate how the economic complexity index (ECI) and fitness index (FI) are related directly to economic fundamentals with a clear basis in theory. The model is based on Eaton and Kortum (2002 Econometrica 70 1741–79) and combines factor endowment (Heckscher-Ohlin) and technological (Ricardian) reasons for specialization, which further determines economic complexity across countries. First, we find that FI performs better than ECI in explaining the real-world specialization pattern, where successful countries not only produce complex products due to the comparative advantage but also tend to produce a wide range of possible products due to the absolute advantage. Second, we highlight that the predictive power of various economic complexity measures for income is crucially sensitive to other factors that shift marginal cost from its efficient level in manufacturing sectors. The essence of such an issue lies in the assumption that the revealed comparative advantage (RCA) correctly reflects a country’s real capability of specialization across different goods. However, there would exist a gap between the core idea of learning the national complexity from RCA and the fact that the revealed specialization pattern in data may not necessarily suggest a country’s actual capability in the presence of distortions, the latter of which is ubiquitous across developing countries.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43852736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stasis in heterogeneous networks of coupled oscillators: discontinuous transition with hysteresis","authors":"Samir Sahoo, A. Prasad, R. Ramaswamy","doi":"10.1088/2632-072X/ace1c4","DOIUrl":"https://doi.org/10.1088/2632-072X/ace1c4","url":null,"abstract":"We consider a heterogeneous ensemble of dynamical systems in R4 that individually are either attracted to fixed points (and are termed inactive) or to limit cycles (in which case they are termed active). These distinct states are separated by bifurcations that are controlled by a single parameter. Upon coupling them globally, we find a discontinuous transition to global inactivity (or stasis) when the proportion of inactive components in the ensemble exceeds a threshold: there is a first–order phase transition from a globally oscillatory state to global oscillation death. There is hysteresis associated with these phase transitions. Numerical results for a representative system are supported by analysis using a system-reduction technique and different dynamical regimes can be rationalised through the corresponding bifurcation diagrams of the reduced set of equations.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45180076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosted fluctuation responses in power grids with active voltage dynamics","authors":"Moritz Thümler, M. Timme","doi":"10.1088/2632-072X/acdb26","DOIUrl":"https://doi.org/10.1088/2632-072X/acdb26","url":null,"abstract":"Secure electric energy supply and thus stable operation of power grids fundamentally relies on their capability to cope with fluctuations. Here, we study how active voltage dynamics impacts the collective response dynamics of networked power grids. We find that the systems driven by ongoing fluctuating inputs exhibit a bulk, a resonance, and a localized grid frequency response regime, as for static voltages. However, active voltage dynamics generically weakens the degree of localization in the grid, thereby intensifying and spatially extending the high-frequency responses. An analytic approximation scheme that takes into account shortest signal propagation paths among the voltage, phase angle and frequency variables result in an asymptotic lowest-order expansion that helps understanding the boosted high-frequency responses. These results moreover offer a generic tool to systematically investigate fluctuation response patterns in power grid models with and without active voltage dynamics.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48156279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systems","authors":"Rishita Das, M. Porfiri","doi":"10.1088/2632-072X/acde2d","DOIUrl":"https://doi.org/10.1088/2632-072X/acde2d","url":null,"abstract":"Transfer entropy is emerging as the statistical approach of choice to support the inference of causal interactions in complex systems from time-series of their individual units. With reference to a simple dyadic system composed of two coupled units, the successful application of net transfer entropy-based inference relies on unidirectional coupling between the units and their homogeneous dynamics. What happens when the units are bidirectionally coupled and have different dynamics? Through analytical and numerical insights, we show that net transfer entropy may lead to erroneous inference of the dominant direction of influence that stems from its dependence on the units’ individual dynamics. To control for these confounding effects, one should incorporate further knowledge about the units’ time-histories through the recent framework offered by momentary information transfer. In this realm, we demonstrate the use of two measures: controlled and fully controlled transfer entropies, which consistently yield the correct direction of dominant coupling irrespective of the sources and targets individual dynamics. Through the study of two real-world examples, we identify critical limitations with respect to the use of net transfer entropy in the inference of causal mechanisms that warrant prudence by the community.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45952818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Laplacian dynamics of convergent and divergent collective behaviors","authors":"Yang Tian, Yunhui Xu, Pei Sun","doi":"10.1088/2632-072X/acd6cb","DOIUrl":"https://doi.org/10.1088/2632-072X/acd6cb","url":null,"abstract":"Collective dynamics is ubiquitous in various physical, biological, and social systems, where simple local interactions between individual units lead to complex global patterns. A common feature of diverse collective behaviors is that the units exhibit either convergent or divergent evolution in their behaviors, i.e. becoming increasingly similar or distinct, respectively. The associated dynamics changes across time, leading to complex consequences on a global scale. In this study, we propose a generalized Laplacian dynamics model to describe both convergent and divergent collective behaviors, where the trends of convergence and divergence compete with each other and jointly determine the evolution of global patterns. We empirically observe non-trivial phase-transition-like phenomena between the convergent and divergent evolution phases, which are controlled by local interaction properties. We also propose a conjecture regarding the underlying phase transition mechanisms and outline the main theoretical difficulties for testing this conjecture. Overall, our framework may serve as a minimal model of collective behaviors and their intricate dynamics.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46225837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Syndication network associates with specialisation and performance of venture capital firms","authors":"Qing Yao, Shaodong Ma, Jingru Liang, Kim Christensen, Wang Jing, Ruiqi Li","doi":"10.1088/2632-072X/acd6cc","DOIUrl":"https://doi.org/10.1088/2632-072X/acd6cc","url":null,"abstract":"The Chinese venture capital (VC) market is a young and rapidly expanding financial subsector. Gaining a deeper understanding of the investment behaviours of VC firms is crucial for the development of a more sustainable and healthier market and economy. Contrasting evidence supports that either specialisation or diversification helps to achieve a better investment performance. However, the impact of the syndication network is overlooked. Syndication network has a great influence on the propagation of information and trust. By exploiting an authoritative VC dataset of thirty-five-year investment information in China, we construct a joint-investment network of VC firms and analyse the impacts of syndication and diversification on specialisation and investment performance. There is a clear correlation between the syndication network degree and specialisation level of VC firms, which implies that the well-connected VC firms are diversified. More connections generally bring about more information or other resources, and VC firms are more likely to enter a new stage or industry with some new co-investing VC firms when compared to a randomised null model. Moreover, autocorrelation analysis of both specialisation and success rate on the syndication network indicates that feature clustering of similar VC firms is roughly limited to the secondary neighbourhood. When analysing local feature clustering patterns, we discover that, contrary to popular beliefs, there is no apparent successful club of investors. In contrast, investors with low success rates are more likely to cluster. Our discoveries enrich the understanding of VC investment behaviours and can assist policymakers in designing better strategies to promote the development of the VC industry.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44357543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Entropy of microcanonical finite-graph ensembles","authors":"T. Kawamoto","doi":"10.1088/2632-072X/acf01c","DOIUrl":"https://doi.org/10.1088/2632-072X/acf01c","url":null,"abstract":"The entropy of random graph ensembles has gained widespread attention in the field of graph theory and network science. We consider microcanonical ensembles of simple graphs with prescribed degree sequences. We demonstrate that the mean-field approximations of the generating function using the Chebyshev–Hermite polynomials provide estimates for the entropy of finite-graph ensembles. Our estimate reproduces the Bender–Canfield formula in the limit of large graphs.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"4 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41803813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}