arXiv subjects

Rosario N. Mantegna

Publications and source records attributed to Rosario N. Mantegna.

At least 19 recordsLinked to original sources

arXiv2026

Social Physics: A manifesto

Social Physics seeks quantitative, empirically testable explanations of collective human behavior. Its name has a long and contested history, but its contemporary program is neither the claim that society is literally a physical system nor an attempt to replace the social sciences with physics. It is an interdisciplinary practice: observation and experimentation, model construction, mathematical and computational analysis, and repeated confrontation with data. The field has been transformed by analyzing digital traces, network science, agent-based modeling, large-scale experiments, and the capacity to study coupled dynamics across economic, social, technological, biological, and physical layers. We review advances in financial analysis and modeling; economic complexity; the structure and evolution of social networks; spreading, opinion, and information dynamics; inequality, cooperation, conflict, mobility, cities, culture, science, and collective behavior; and the emerging study of human-AI ecosystems. Across these domains, common features recur - heterogeneity, interaction, feedback, adaptation, non-equilibrium dynamics, multiscale organization, and emergent collective outcomes. We also confront the limitations that have arisen in this field: convenience samples, platform dependence, weak causal identification, over-universal claims, insufficient engagement with meaning and institutions, privacy risks, and the danger that predictive models become instruments of manipulation. We propose a future Social Physics organized around theory - data reciprocity; causal and generative explanation; measurement validity and cross-context generalization; responsible, participatory data governance; genuinely interdisciplinary collaboration; and reflexive study of interventions and algorithms that alter the systems being observed.

physics.soc-ph↗

arXiv2026

Identifying maximal sets of significantly interacting nodes in higher-order networks

Filtering methods are fundamental tools for extracting the backbone of complex networks. Systems displaying group interactions, however, open the way to new types of filtering approaches. Here, we introduce a statistical filtering method for higher-order networks that identifies statistically validated maximal interacting sets -- maximal sets of nodes that consistently interact together within group interactions. Using properly designed benchmarks, we show that our approach is highly effective in systems where the maximal sets are likely to be diluted into interactions of larger sizes that include occasional participants. Applications to real-world data reveal that the identified sets of nodes are characterized by higher levels of similarity and topical coherence, highlighting the ability of our method to provide new insights on the organization of real-world higher-order networks.

physics.soc-ph↗

arXiv2026

How null-model constraints affect statistical validation in projected bipartite networks

Statistical validation of projected bipartite networks depends critically on the null model adopted to describe random co-occurrences. Although several null models have been proposed, their comparison has mainly focused on the validated backbones they produce rather than on the statistical assumptions underlying their construction. Here we compare four widely used null models - the microcanonical configuration model generated by the Curveball algorithm, the Bipartite Configuration Model (BiCM), the Bipartite Partial Configuration Model (BiPCM), and the Hypergeometric approximation - using three empirical bipartite systems from comparative genomics, international trade and food science. We use the statistically validated links obtained from the microcanonical bipartite configuration model as the reference benchmark for assessing performances of the other three models. Our central result is that the statistical consequences of relaxing null-model constraints cannot be understood solely from the constraints themselves but must be analyzed through the probability distribution induced for the co-occurrence statistic. In particular, the combined behaviour of the expectation and variance largely explains the observed differences among the statistically validated backbones. We further derive a leading-order sparse approximation for the BiCM expectation, showing that the first correction to the Hypergeometric prediction is controlled by the degree heterogeneity of the non-projected layer. Surprisingly, despite neglecting this heterogeneity, the Hypergeometric model accurately reproduces the co-occurrence variance of the microcanonical ensemble across all datasets. Our results suggest that null models should be compared not only according to the constraints they preserve but also according to the statistical consequences that these constraints induce on the distribution of the test statistic.

physics.soc-ph↗

arXiv2026

Was Benoit Mandelbrot a hedgehog or a fox?

Benoit Mandelbrot's scientific legacy spans an extraordinary range of disciplines, from linguistics and fluid turbulence to cosmology and finance, suggesting the intellectual temperament of a "fox" in Isaiah Berlin's famous dichotomy of thinkers. This essay argues, however, that Mandelbrot was, at heart, a "hedgehog": a thinker unified by a single guiding principle. Across his diverse pursuits, the concept of scaling -- manifested in self-similarity, power laws, fractals, and multifractals -- served as the central idea that structured his work. By tracing the continuity of this scaling paradigm through his contributions to mathematics, physics, and economics, the paper reveals a coherent intellectual trajectory masked by apparent eclecticism. Mandelbrot's enduring insight in the modeling of natural and social phenomena can be understood through the lens of the geometry and statistics of scale invariance.

physics.soc-ph↗

arXiv2025

Large scale statistically validated comorbidity networks

We obtain comorbidity networks starting from medical information stored in electronic health records collected by the Wellbeing Services County of Southwest Finland (Varha). Based on the data, we associate each patient to one or more diseases and construct complex comorbidity networks associated with large patient cohorts characterized by an age interval and sex. The information about diseases in electronic health records is coded using the highest granularity present in the international classification of diseases (ICD codes) provided by the World Health Organization. We statistically validate links in each cohort comorbidity network and furthermore partition the networks into communities of diseases. These are characterized by the over-expression of a few disease categories, and communities from different age or sex cohorts show various similarities in terms of these disease classes. Moreover, all the detected communities for all the cohorts can be organized into a hierarchical tree. This allows us to observe a number of clusters of communities, originating from diverse age and sex cohorts, that group together communities characterized by the same disease classes. We also perform a dismantling procedure of statistically validated comorbidity networks to highlight those categories of diseases that are most responsible for the compactedness of the comorbidity networks for a given cohort of patients.

physics.soc-ph↗

arXiv2025

Heterogeneity of household stock portfolios in a national market

We study the long term dynamics of the stock portfolios owned by single Finnish legal entities in the Helsinki venue of the Nasdaq Nordic between 2001 and 2021. Using the Herfindahl-Hirschman index as a measure of concentration for the composition of stock portfolios, we investigate the concentration of Finnish household portfolios both at the level of each individual household and tracking the time evolution of an aggregated Finnish household portfolio. We also consider aggregated portfolios of two other macro categories of investors one comprising Finnish institutional investors and the other comprising foreign investors. Different macro categories of investors present a different degree of concentration of aggregated stock portfolios with highest concentration observed for foreign investors. For individual Finnish retail investors, portfolio concentration estimated by the Herfindahl-Hirschman index presents high values for more than half of the total number of retail investors. In spite of the observation that retail stock portfolios are often composed by just a few stocks, the concentration of the aggregated stock portfolio for Finnish retail investors has a portfolio concentration comparable with the one of Finnish institutional investors. Within retail investors, stock portfolios of women present a similar pattern of portfolios of men but with a systematic higher level of concentration observed for women both at individual and at aggregated level.

q-fin.GN↗

arXiv2023

Two-step estimators of high dimensional correlation matrices

We investigate block diagonal and hierarchical nested stochastic multivariate Gaussian models by studying their sample cross-correlation matrix on high dimensions. By performing numerical simulations, we compare a filtered sample cross-correlation with the population cross-correlation matrices by using several rotationally invariant estimators (RIE) and hierarchical clustering estimators (HCE) under several loss functions. We show that at large but finite sample size, sample cross-correlation filtered by RIE estimators are often outperformed by HCE estimators for several of the loss functions. We also show that for block models and for hierarchically nested block models the best determination of the filtered sample cross-correlation is achieved by introducing two-step estimators combining state-of-the-art non-linear shrinkage models with hierarchical clustering estimators.

stat.ME↗

arXiv2021

Quantifying the relationship between specialisation and reputation in an online platform

Online platforms experience a tension between decentralisation and incentives to steer user behaviour, which are usually implemented through digital reputation systems. We provide a statistical characterisation of the user behaviour emerging from the interplay of such competing forces in Stack Overflow, a long-standing knowledge sharing platform. Over the 11 years covered by our analysis, we find that the platform's user base consistently self-organise into specialists and generalists, i.e., users who focus their activity on narrow and broad sets of topics, respectively. We relate the emergence of these behaviours to the platform's reputation system with a series of data-driven models, and find specialisation to be statistically associated with a higher ability to post the best answers to a question. Our findings are in stark contrast with observations made in top-down environments - such as firms and corporations - where generalist skills are consistently found to be more successful.

physics.soc-ph↗

arXiv2021

Detecting informative higher-order interactions in statistically validated hypergraphs

Recent empirical evidence has shown that in many real-world systems, successfully represented as networks, interactions are not limited to dyads, but often involve three or more agents at a time. These data are better described by hypergraphs, where hyperlinks encode higher-order interactions among a group of nodes. In spite of the large number of works on networks, highlighting informative hyperlinks in hypergraphs obtained from real world data is still an open problem. Here we propose an analytic approach to filter hypergraphs by identifying those hyperlinks that are over-expressed with respect to a random null hypothesis, and represent the most relevant higher-order connections. We apply our method to a class of synthetic benchmarks and to several datasets. For all cases, the method highlights hyperlinks that are more informative than those extracted with pairwise approaches. Our method provides a first way to obtain statistically validated hypergraphs, separating informative connections from redundant and noisy ones.

physics.soc-ph↗

arXiv2020

Dynamics of fintech terms in news and blogs and specialization of companies of the fintech industry

We perform a large scale analysis of a list of fintech terms in (i) news and blogs in English language and (ii) professional descriptions of companies operating in many countries. The occurrence and co-occurrence of fintech terms and locutions shows a progressive evolution of the list of fintech terms in a compact and coherent set of terms used worldwide to describe fintech business activities. By using methods of complex networks that are specifically designed to deal with heterogeneous systems, our analysis of a large set of professional descriptions of companies shows that companies having fintech terms in their description present over-expressions of specific attributes of country, municipality, and economic sector. By using the approach of statistically validated networks, we detect geographical and economic over-expressions of a set of companies related to the multi-industry, geographically and economically distributed fintech movement.

physics.soc-ph↗

arXiv2019

Nested partitions from hierarchical clustering statistical validation

We develop a greedy algorithm that is fast and scalable in the detection of a nested partition extracted from a dendrogram obtained from hierarchical clustering of a multivariate series. Our algorithm provides a $p$-value for each clade observed in the hierarchical tree. The $p$-value is obtained by computing a number of bootstrap replicas of the dissimilarity matrix and by performing a statistical test on each difference between the dissimilarity associated with a given clade and the dissimilarity of the clade of its parent node. We prove the efficacy of our algorithm with a set of benchmarks generated by using a hierarchical factor model. We compare the results obtained by our algorithm with those of Pvclust. Pvclust is a widely used algorithm developed with a global approach originally motivated by phylogenetic studies. In our numerical experiments we focus on the role of multiple hypothesis test correction and on the robustness of the algorithms to inaccuracy and errors of datasets. We also apply our algorithm to a reference empirical dataset. We verify that our algorithm is much faster than Pvclust algorithm and has a better scalability both in the number of elements and in the number of records of the investigated multivariate set. Our algorithm provides a hierarchically nested partition in much shorter time than currently widely used algorithms allowing to perform a statistically validated cluster analysis detection in very large systems.

q-bio.GN↗

arXiv2018

Bootstrap validation of links of a minimum spanning tree

We describe two different bootstrap methods applied to the detection of a minimum spanning tree obtained from a set of multivariate variables. We show that two different bootstrap procedures provide partly distinct information that can be highly informative about the investigated complex system. Our case study, based on the investigation of daily returns of a portfolio of stocks traded in the US equity markets, shows the degree of robustness and completeness of the information extracted with popular information filtering methods such as the minimum spanning tree and the planar maximally filtered graph. The first method performs a "row bootstrap" whereas the second method performs a "pair bootstrap". We show that the parallel use of the two methods is suggested especially for complex systems presenting both a nested hierarchical organization together with the presence of global feedback channels.

stat.ME↗

arXiv2017

Core of communities in bipartite networks

We use the information present in a bipartite network to detect cores of communities of each set of the bipartite system. Cores of communities are found by investigating statistically validated projected networks obtained using information present in the bipartite network. Cores of communities are highly informative and robust with respect to the presence of errors or missing entries in the bipartite network. We assess the statistical robustness of cores by investigating an artificial benchmark network, the co-authorship network, and the actor-movie network. The accuracy and precision of the partition obtained with respect to the reference partition are measured in terms of the adjusted Rand index and of the adjusted Wallace index respectively. The detection of cores is highly precise although the accuracy of the methodology can be limited in some cases.

physics.soc-ph↗

arXiv2016

An empirically grounded agent based model for modeling directs, conflict detection and resolution operations in Air Traffic Management

We present an agent based model of the Air Traffic Management socio-technical complex system that aims at modeling the interactions between aircrafts and air traffic controllers at a tactical level. The core of the model is given by the conflict detection and resolution module and by the directs module. Directs are flight shortcuts that are given by air controllers to speed up the passage of an aircraft within a certain airspace and therefore to facilitate airline operations. Conflicts resolution between flight trajectories can arise during the en-route phase of each flight due to both not detailed flight trajectory planning or unforeseen events that perturb the planned flight plan. Our model performs a local conflict detection and resolution procedure. Once a flight trajectory has been made conflict-free, the model searches for possible improvements of the system efficiency by issuing directs. We give an example of model calibration based on real data. We then provide an illustration of the capability of our model in generating scenario simulations able to give insights about the air traffic management system. We show that the calibrated model is able to reproduce the existence of a geographical localization of air traffic controllers' operations. Finally, we use the model to investigate the relationship between directs and conflict resolutions (i) in the presence of perfect forecast ability of controllers, and (ii) in the presence of some degree of uncertainty in flight trajectory forecast.

physics.soc-ph↗

arXiv2015

Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach

We investigate the trading behavior of Finnish individual investors trading the stocks selected to compute the OMXH25 index in 2003 by tracking the individual daily investment decisions. We verify that the set of investors is a highly heterogeneous system under many aspects. We introduce a correlation based method that is able to detect a hierarchical structure of the trading profiles of heterogeneous individual investors. We verify that the detected hierarchical structure is highly overlapping with the cluster structure obtained with the approach of statistically validated networks when an appropriate threshold of the hierarchical trees is used. We also show that the combination of the correlation based method and of the statistically validated method provides a way to expand the information about the clusters of investors with similar trading profiles in a robust and reliable way.

q-fin.TR↗

arXiv2015

Backbone of credit relationships in the Japanese credit market

We detect the backbone of the weighted bipartite network of the Japanese credit market relationships. The backbone is detected by adapting a general method used in the investigation of weighted networks. With this approach we detect a backbone that is statistically validated against a null hypothesis of uniform diversification of loans for banks and firms. Our investigation is done year by year and it covers more than thirty years during the period from 1980 to 2011. We relate some of our findings with economic events that have characterized the Japanese credit market during the last years. The study of the time evolution of the backbone allows us to detect changes occurred in network size, fraction of credit explained, and attributes characterizing the banks and the firms present in the backbone.

q-fin.GN↗

arXiv2014

Sicily and the development of Econophysics: the pioneering work of Ettore Majorana and the Econophysics Workshop in Palermo

Sicily has played an important role in the development of the new research area named "Econophysics". In fact some key ideas supporting this new hybrid discipline were originally formulated in a pioneering work of the Sicilian born physicist Ettore Majorana. The article he wrote was entitled "The value of statistical laws in physics and social sciences". I will discuss its origin and history that has been recently discovered in the study of Stefano Roncoroni. This recent study documents the true reasons and motivations that triggered the pioneering work of Majorana. It also shows that the description of this work provided by Edoardo Amaldi was shallow and misleading. In the second part of the talk I will recollect the first years of development of econophysics and in particular the role of the "International Workshop on Econophysics and Statistical Finance" held in Palermo on 28-30 September 1998 and the setting in 1999 of the "Observatory of Complex Systems" the research group on Econophysics of Palermo University and Istituto Nazionale di Fisica della Materia.

physics.hist-ph↗

arXiv2014

Bank-firm credit network in Japan. An analysis of a bipartite network

We present an analysis of the credit market of Japan. The analysis is performed by investigating the bipartite network of banks and firms which is obtained by setting a link between a bank and a firm when a credit relationship is present in a given time window. In our investigation we focus on a community detection algorithm which is identifying communities composed by both banks and firms. We show that the clusters obtained by directly working on the bipartite network carry information about the networked nature of the Japanese credit market. Our analysis is performed for each calendar year during the time period from 1980 to 2011. Specifically, we obtain communities of banks and networks for each of the 32 investigated years, and we introduce a method to track the time evolution of these communities on a statistical basis. We then characterize communities by detecting the simultaneous over-expression of attributes of firms and banks. Specifically, we consider as attributes the economic sector and the geographical location of firms and the type of banks. In our 32 year long analysis we detect a persistence of the over-expression of attributes of clusters of banks and firms together with a slow dynamics of changes from some specific attributes to new ones. Our empirical observations show that the credit market in Japan is a networked market where the type of banks, geographical location of firms and banks and economic sector of the firm play a role in shaping the credit relationships between banks and firms.

q-fin.GN↗