Pub Date : 2024-02-16DOI: 10.1088/2632-072x/ad2720
José R Nicolás-Carlock, Denis Boyer, Sandra E Smith-Aguilar, Gabriel Ramos-Fernández
Homophily describes a fundamental tie-formation mechanism in social networks in which connections between similar nodes occur at a higher rate than among dissimilar ones. In this article, we present an extension of the weighted social network (WSN) model that, under an explicit homophily principle, quantifies the emergence of attribute-dependent properties of a social system. To test our model, we make use of empirical association data of a group of free-ranging spider monkeys in Yucatan, Mexico. Our homophilic WSN model reproduces many of the properties of the empirical association network with statistical significance, specifically, the average weight of sex-dependent interactions (female-female, female-male, male-male), the weight distribution function, as well as many weighted macro properties (node strength, weighted clustering, and weighted number of modules), even for different age group combinations (adults, subadults, and juveniles). Furthermore, by performing simulations with fitted parameters, we show that one of the main features of a spider monkey social system, namely, stronger male-male interactions over female-female or female-male ones, can be accounted for by an asymmetry in the node-type composition of a bipartisan network, independently of group size. The reinforcement of connections among members of minority groups could be a general structuring mechanism in homophilic social networks.
{"title":"Strength of minority ties: the role of homophily and group composition in a weighted social network","authors":"José R Nicolás-Carlock, Denis Boyer, Sandra E Smith-Aguilar, Gabriel Ramos-Fernández","doi":"10.1088/2632-072x/ad2720","DOIUrl":"https://doi.org/10.1088/2632-072x/ad2720","url":null,"abstract":"Homophily describes a fundamental tie-formation mechanism in social networks in which connections between similar nodes occur at a higher rate than among dissimilar ones. In this article, we present an extension of the weighted social network (WSN) model that, under an explicit homophily principle, quantifies the emergence of attribute-dependent properties of a social system. To test our model, we make use of empirical association data of a group of free-ranging spider monkeys in Yucatan, Mexico. Our homophilic WSN model reproduces many of the properties of the empirical association network with statistical significance, specifically, the average weight of sex-dependent interactions (female-female, female-male, male-male), the weight distribution function, as well as many weighted macro properties (node strength, weighted clustering, and weighted number of modules), even for different age group combinations (adults, subadults, and juveniles). Furthermore, by performing simulations with fitted parameters, we show that one of the main features of a spider monkey social system, namely, stronger male-male interactions over female-female or female-male ones, can be accounted for by an asymmetry in the node-type composition of a bipartisan network, independently of group size. The reinforcement of connections among members of minority groups could be a general structuring mechanism in homophilic social networks.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"15 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140011039","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}
Pub Date : 2024-02-14DOI: 10.1088/2632-072x/ad2372
Antonio Alfonso, Pablo Brañas-Garza, Antonio Cabrales, Angel Sánchez
We have studied the problem of climate change mitigation in large groups by means of a series of experiments with 1785 people. Our participants included both young university students and people of relevance in different organizations, in particular, those attending the presentation of the annual report on innovation by Fundación COTEC (Spain). In the experiment, the participants, distributed in groups of more than 100 people, faced a dilemma: to avoid a global catastrophe that destroys any possibility of making profits, a certain collective sacrifice has to be made by contributing to reach a global threshold. When the threshold was low, the students reached the amount of overall contribution necessary to avoid it. But in the case of a high threshold, none of the populations reached the threshold. In fact, they were far from it. In this sense, the collective behavior of the students and of people of relevance was fundamentally the same. The majority of participants in the high-risk case fell into four categories: those who did not contribute (around 10%), those who contribute half of their means (15%) but less than the fair share required to reach the threshold, those who contributed the fair share (10%), and those who contributed everything they had, so that their personal benefit was zero. In the case of students this last percentage was 10%, but in the other sample it reached almost 30%. We also found that individuals could be classified as being optimistic or pessimistic, and in general they behaved accordingly with regard to their contributions. Our results highlight the complexity of mitigating climate change in large groups and specially the difficulty in communicating the issue to foster action in a general population.
{"title":"The complexity of climate change mitigation: an experiment with large groups","authors":"Antonio Alfonso, Pablo Brañas-Garza, Antonio Cabrales, Angel Sánchez","doi":"10.1088/2632-072x/ad2372","DOIUrl":"https://doi.org/10.1088/2632-072x/ad2372","url":null,"abstract":"We have studied the problem of climate change mitigation in large groups by means of a series of experiments with 1785 people. Our participants included both young university students and people of relevance in different organizations, in particular, those attending the presentation of the annual report on innovation by Fundación COTEC (Spain). In the experiment, the participants, distributed in groups of more than 100 people, faced a dilemma: to avoid a global catastrophe that destroys any possibility of making profits, a certain collective sacrifice has to be made by contributing to reach a global threshold. When the threshold was low, the students reached the amount of overall contribution necessary to avoid it. But in the case of a high threshold, none of the populations reached the threshold. In fact, they were far from it. In this sense, the collective behavior of the students and of people of relevance was fundamentally the same. The majority of participants in the high-risk case fell into four categories: those who did not contribute (around 10%), those who contribute half of their means (15%) but less than the fair share required to reach the threshold, those who contributed the fair share (10%), and those who contributed everything they had, so that their personal benefit was zero. In the case of students this last percentage was 10%, but in the other sample it reached almost 30%. We also found that individuals could be classified as being optimistic or pessimistic, and in general they behaved accordingly with regard to their contributions. Our results highlight the complexity of mitigating climate change in large groups and specially the difficulty in communicating the issue to foster action in a general population.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"141 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140011032","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}
Pub Date : 2024-02-13DOI: 10.1088/2632-072x/ad253a
Nicholas W Landry, Ilya Amburg, Mirah Shi, Sinan G Aksoy
Many complex systems often contain interactions between more than two nodes, known as higher-order interactions, which can change the structure of these systems in significant ways. Researchers often assume that all interactions paint a consistent picture of a higher-order dataset’s structure. In contrast, the connection patterns of individuals or entities in empirical systems are often stratified by interaction size. Ignoring this fact can aggregate connection patterns that exist only at certain scales of interaction. To isolate these scale-dependent patterns, we present an approach for analyzing higher-order datasets by filtering interactions by their size. We apply this framework to several empirical datasets from three domains to demonstrate that data practitioners can gain valuable information from this approach.
{"title":"Filtering higher-order datasets","authors":"Nicholas W Landry, Ilya Amburg, Mirah Shi, Sinan G Aksoy","doi":"10.1088/2632-072x/ad253a","DOIUrl":"https://doi.org/10.1088/2632-072x/ad253a","url":null,"abstract":"Many complex systems often contain interactions between more than two nodes, known as <italic toggle=\"yes\">higher-order interactions</italic>, which can change the structure of these systems in significant ways. Researchers often assume that all interactions paint a consistent picture of a higher-order dataset’s structure. In contrast, the connection patterns of individuals or entities in empirical systems are often stratified by interaction size. Ignoring this fact can aggregate connection patterns that exist only at certain scales of interaction. To isolate these scale-dependent patterns, we present an approach for analyzing higher-order datasets by filtering interactions by their size. We apply this framework to several empirical datasets from three domains to demonstrate that data practitioners can gain valuable information from this approach.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"170 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140010933","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}
Pub Date : 2024-01-16DOI: 10.1088/2632-072x/ad1a1a
Sven Benjamin Kožić, Salvatore Marco Giampaolo, Vinko Zlatić
A framework for studying the behavior of a classically frustrated signed network in the process of random rewiring is developed. We describe jump probabilities for change in frustration and formulate a theoretical estimate in terms of the master equation. Stationary thermodynamic distribution and moments are derived from the master equation and compared to numerical simulations. Furthermore, an exact solution of the probability distribution is provided through suitable mapping of rewiring dynamic to birth and death processes with quadratic asymptotically symmetric transition rates.
{"title":"Rewiring driven evolution of quenched frustrated signed network","authors":"Sven Benjamin Kožić, Salvatore Marco Giampaolo, Vinko Zlatić","doi":"10.1088/2632-072x/ad1a1a","DOIUrl":"https://doi.org/10.1088/2632-072x/ad1a1a","url":null,"abstract":"A framework for studying the behavior of a classically frustrated signed network in the process of random rewiring is developed. We describe jump probabilities for change in frustration and formulate a theoretical estimate in terms of the master equation. Stationary thermodynamic distribution and moments are derived from the master equation and compared to numerical simulations. Furthermore, an exact solution of the probability distribution is provided through suitable mapping of rewiring dynamic to birth and death processes with quadratic asymptotically symmetric transition rates.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"59 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139509156","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}
Pub Date : 2024-01-09DOI: 10.1088/2632-072x/ad19e0
Samuel Johnson
‘Compartmental models’ of epidemics are widely used to forecast the effects of communicable diseases such as COVID-19 and to guide policy. Although it has long been known that such processes take place on social networks, the assumption of ‘random mixing’ is usually made, which ignores network structure. However, ‘super-spreading events’ have been found to be power-law distributed, suggesting that the underlying networks may be scale free or at least highly heterogeneous. The random-mixing assumption would then produce an overestimation of the herd-immunity threshold for given R0; and a (more significant) overestimation of R0 itself. These two errors compound each other, and can lead to forecasts greatly overestimating the number of infections. Moreover, if networks are heterogeneous and change in time, multiple waves of infection can occur, which are not predicted by random mixing. A simple SIR model simulated on both Erdős–Rényi and scale-free networks shows that details of the network structure can be more important than the intrinsic transmissibility of a disease. It is therefore crucial to incorporate network information into standard models of epidemics.
{"title":"Epidemic modelling requires knowledge of the social network","authors":"Samuel Johnson","doi":"10.1088/2632-072x/ad19e0","DOIUrl":"https://doi.org/10.1088/2632-072x/ad19e0","url":null,"abstract":"‘Compartmental models’ of epidemics are widely used to forecast the effects of communicable diseases such as COVID-19 and to guide policy. Although it has long been known that such processes take place on social networks, the assumption of ‘random mixing’ is usually made, which ignores network structure. However, ‘super-spreading events’ have been found to be power-law distributed, suggesting that the underlying networks may be scale free or at least highly heterogeneous. The random-mixing assumption would then produce an overestimation of the herd-immunity threshold for given <italic toggle=\"yes\">R</italic>\u0000<sub>0</sub>; and a (more significant) overestimation of <italic toggle=\"yes\">R</italic>\u0000<sub>0</sub> itself. These two errors compound each other, and can lead to forecasts greatly overestimating the number of infections. Moreover, if networks are heterogeneous and change in time, multiple waves of infection can occur, which are not predicted by random mixing. A simple SIR model simulated on both Erdős–Rényi and scale-free networks shows that details of the network structure can be more important than the intrinsic transmissibility of a disease. It is therefore crucial to incorporate network information into standard models of epidemics.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"33 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139509173","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}
We revise the procedure proposed by Balassa to infer comparative advantage, which is a standard tool in Economics to analyze specialization (of countries, regions, etc). Balassa’s approach compares a country’s export of a given product with what would be expected from a benchmark based on the total volumes of countries and product flows. Based on results in the literature, we show that implementing Balassa’s idea leads to conditions for estimating parameters conflicting with the information content of the model itself. Moreover, Balassa’s approach does not implement any statistical validation. Hence, we propose an alternative procedure to overcome such a limitation, based upon the framework of entropy maximization and implementing a proper test of hypothesis: the ‘key products’ of a country are, now, the ones whose production is significantly larger than expected, under a null-model constraining the same amount of information defining Balassa’s approach. What we found is that country diversification is always observed, regardless of the strictness of the validation procedure. Besides, the ranking of countries’ fitnesses is only partially affected by the details of the validation scheme employed for the analysis while large differences are found to affect the rankings of product complexities. The routine for implementing the entropy-based filtering procedures employed here is freely available through the official Python Package Index PyPI.
{"title":"Inferring comparative advantage via entropy maximization","authors":"Matteo Bruno, Dario Mazzilli, Aurelio Patelli, Tiziano Squartini, Fabio Saracco","doi":"10.1088/2632-072x/ad1411","DOIUrl":"https://doi.org/10.1088/2632-072x/ad1411","url":null,"abstract":"We revise the procedure proposed by Balassa to infer comparative advantage, which is a standard tool in Economics to analyze specialization (of countries, regions, etc). Balassa’s approach compares a country’s export of a given product with what would be expected from a benchmark based on the total volumes of countries and product flows. Based on results in the literature, we show that implementing Balassa’s idea leads to conditions for estimating parameters conflicting with the information content of the model itself. Moreover, Balassa’s approach does not implement any statistical validation. Hence, we propose an alternative procedure to overcome such a limitation, based upon the framework of entropy maximization and implementing a proper test of hypothesis: the ‘key products’ of a country are, now, the ones whose production is significantly larger than expected, under a null-model constraining the same amount of information defining Balassa’s approach. What we found is that country diversification is always observed, regardless of the strictness of the validation procedure. Besides, the ranking of countries’ fitnesses is only partially affected by the details of the validation scheme employed for the analysis while large differences are found to affect the rankings of product complexities. The routine for implementing the entropy-based filtering procedures employed here is freely available through the official Python Package Index <monospace>PyPI</monospace>.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"57 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139093334","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}
Pub Date : 2023-12-27DOI: 10.1088/2632-072x/ad1410
Hariphan Philathong, Vishwanathan Akshay, Igor Zacharov, Jacob Biamonte
Gibbs sampling is fundamental to a wide range of computer algorithms. Such algorithms are set to be replaced by physics based processors—be it quantum or stochastic annealing devices—which embed problem instances and evolve a physical system into a low-energy ensemble to recover a probability distribution. At a critical constraint to variable ratio, satisfiability (SAT) problem instances exhibit a SAT-UNSAT transition (frustrated to frustration free). Algorithms require increasing computational resources from this critical point. This is a so called, algorithmic or computational phase transition and has extensively been studied. In this paper we consider the complexity in sampling and recovering ground states from resultant distributions of a physics based processor. In particular, we first consider the ideal Gibbs distributions at some fixed inverse temperature and observe that the success probability in sampling and recovering ground states decrease for instances starting at the critical density. Furthermore, simulating the Gibbs distribution, we employ Ising spin dynamics, which play a crucial role in understanding of non-equilibrium statistical physics, to find their steady states of 2-SAT Hamiltonians. We observe that beyond the critical density, the probability of sampling ground states decreases. Our results apply to several contemporary devices and provide a means to experimentally probe a signature of the computational phase transition.
{"title":"Computational phase transition signature in Gibbs sampling","authors":"Hariphan Philathong, Vishwanathan Akshay, Igor Zacharov, Jacob Biamonte","doi":"10.1088/2632-072x/ad1410","DOIUrl":"https://doi.org/10.1088/2632-072x/ad1410","url":null,"abstract":"Gibbs sampling is fundamental to a wide range of computer algorithms. Such algorithms are set to be replaced by physics based processors—be it quantum or stochastic annealing devices—which embed problem instances and evolve a physical system into a low-energy ensemble to recover a probability distribution. At a critical constraint to variable ratio, satisfiability (SAT) problem instances exhibit a SAT-UNSAT transition (frustrated to frustration free). Algorithms require increasing computational resources from this critical point. This is a so called, algorithmic or computational phase transition and has extensively been studied. In this paper we consider the complexity in sampling and recovering ground states from resultant distributions of a physics based processor. In particular, we first consider the ideal Gibbs distributions at some fixed inverse temperature and observe that the success probability in sampling and recovering ground states decrease for instances starting at the critical density. Furthermore, simulating the Gibbs distribution, we employ Ising spin dynamics, which play a crucial role in understanding of non-equilibrium statistical physics, to find their steady states of 2-SAT Hamiltonians. We observe that beyond the critical density, the probability of sampling ground states decreases. Our results apply to several contemporary devices and provide a means to experimentally probe a signature of the computational phase transition.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"15 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139052159","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}
Pub Date : 2023-12-08DOI: 10.1088/2632-072x/ad104a
Jean-Philippe Bouchaud
This is the English version of my inaugural lecture at Collège de France in 2021. I reflect on the difficulty of multi-disciplinary research, which often hinges on unexpected epistemological and methodological differences, for example about the scientific status of models. What is the purpose of a model? What are we ultimately trying to establish: rigorous theorems or ad-hoc calculation recipes; absolute truth, or heuristic representations of the world? I argue that the main contribution of statistical physics to social and economic sciences is to make us realise that unexpected behaviour can emerge at the aggregate level, that isolated individuals would never experience. Crises, panics, opinion reversals, the spread of rumours or beliefs, fashion effects and the zeitgeist, but also the existence of money, lasting institutions, social norms and stable societies, must be understood in terms of collective belief and/or trust, self-sustained by interactions, or on the contrary, the rapid collapse of this belief or trust. The appendix contains my opening remarks to the workshop ‘More is Different’, as a tribute to Phil Anderson.
{"title":"From statistical physics to social sciences: the pitfalls of multi-disciplinarity","authors":"Jean-Philippe Bouchaud","doi":"10.1088/2632-072x/ad104a","DOIUrl":"https://doi.org/10.1088/2632-072x/ad104a","url":null,"abstract":"This is the English version of my inaugural lecture at Collège de France in 2021. I reflect on the difficulty of multi-disciplinary research, which often hinges on unexpected epistemological and methodological differences, for example about the scientific status of <italic toggle=\"yes\">models</italic>. What is the purpose of a model? What are we ultimately trying to establish: rigorous theorems or <italic toggle=\"yes\">ad-hoc</italic> calculation recipes; absolute truth, or heuristic representations of the world? I argue that the main contribution of statistical physics to social and economic sciences is to make us realise that unexpected behaviour can emerge at the aggregate level, that isolated individuals would never experience. Crises, panics, opinion reversals, the spread of rumours or beliefs, fashion effects and the <italic toggle=\"yes\">zeitgeist</italic>, but also the existence of money, lasting institutions, social norms and stable societies, must be understood in terms of collective belief and/or trust, self-sustained by interactions, or on the contrary, the rapid collapse of this belief or trust. The appendix contains my opening remarks to the workshop ‘More is Different’, as a tribute to Phil Anderson.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"106 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138692692","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}
Pub Date : 2023-11-30DOI: 10.1088/2632-072x/ad0d88
Till Kahlke, Alexander K Hartmann
The maximum-weight matching problem and the behavior of its energy landscape is numerically investigated. We apply a perturbation method adapted from the analysis of spin glasses. This method provides insight into the complexity of the energy landscape of different ensembles. Erdős–Rényi graphs and ring graphs with randomly added edges are considered, and two types of distributions for the random edge weights are used. Fast and scalable algorithms exist for maximum weight matching, allowing us to study large graphs with more than 105 nodes. Our results show that the structure of the energy landscape for standard ensembles of matching is simple, comparable to the energy landscape of a ferromagnet. Nonetheless, for some of the ensembles presented here, our results allow for the presence of complex energy landscapes in the spirit of a replica-symmetry breaking scenario.
{"title":"Energy landscapes of some matching-problem ensembles","authors":"Till Kahlke, Alexander K Hartmann","doi":"10.1088/2632-072x/ad0d88","DOIUrl":"https://doi.org/10.1088/2632-072x/ad0d88","url":null,"abstract":"The maximum-weight matching problem and the behavior of its energy landscape is numerically investigated. We apply a perturbation method adapted from the analysis of spin glasses. This method provides insight into the complexity of the energy landscape of different ensembles. Erdős–Rényi graphs and ring graphs with randomly added edges are considered, and two types of distributions for the random edge weights are used. Fast and scalable algorithms exist for maximum weight matching, allowing us to study large graphs with more than 10<sup>5</sup> nodes. Our results show that the structure of the energy landscape for standard ensembles of matching is simple, comparable to the energy landscape of a ferromagnet. Nonetheless, for some of the ensembles presented here, our results allow for the presence of complex energy landscapes in the spirit of a replica-symmetry breaking scenario.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"152 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138692568","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}
Pub Date : 2023-11-29DOI: 10.1088/2632-072x/ad0e23
A Baptista, R J Sánchez-García, A Baudot, G Bianconi
Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted great interest in the past few decades, and has been efficiently applied to fundamental problems in network inference, such as link prediction, node classification, and community detection. In this review, we provide a user-friendly guide to the network embedding literature and current trends in this field which will allow the reader to navigate through the complex landscape of methods and approaches emerging from the vibrant research activity on these subjects.
{"title":"Zoo guide to network embedding","authors":"A Baptista, R J Sánchez-García, A Baudot, G Bianconi","doi":"10.1088/2632-072x/ad0e23","DOIUrl":"https://doi.org/10.1088/2632-072x/ad0e23","url":null,"abstract":"Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted great interest in the past few decades, and has been efficiently applied to fundamental problems in network inference, such as link prediction, node classification, and community detection. In this review, we provide a user-friendly guide to the network embedding literature and current trends in this field which will allow the reader to navigate through the complex landscape of methods and approaches emerging from the vibrant research activity on these subjects.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":"42 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138692566","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}
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