Pub Date : 2022-03-01DOI: 10.1016/j.jco.2022.101654
M. Gnewuch, M. Hefter, A. Hinrichs, K. Ritter
{"title":"Countable tensor products of Hermite spaces and spaces of Gaussian kernels","authors":"M. Gnewuch, M. Hefter, A. Hinrichs, K. Ritter","doi":"10.1016/j.jco.2022.101654","DOIUrl":"https://doi.org/10.1016/j.jco.2022.101654","url":null,"abstract":"","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"1 1","pages":"101654"},"PeriodicalIF":2.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89191902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1016/j.jco.2022.101661
E. Novak, J. Prochno, Mario Ullrich
{"title":"Approximation and Geometry in High Dimensions","authors":"E. Novak, J. Prochno, Mario Ullrich","doi":"10.1016/j.jco.2022.101661","DOIUrl":"https://doi.org/10.1016/j.jco.2022.101661","url":null,"abstract":"","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"522 1","pages":"101661"},"PeriodicalIF":2.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86885152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-01DOI: 10.1016/s0885-064x(21)00079-0
E. Novak
{"title":"Michaela Szölgyenyi is the winner of the 2021 Joseph F. Traub Information-Based Complexity Young Researcher Award","authors":"E. Novak","doi":"10.1016/s0885-064x(21)00079-0","DOIUrl":"https://doi.org/10.1016/s0885-064x(21)00079-0","url":null,"abstract":"","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"111 1","pages":"101624"},"PeriodicalIF":2.1,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72882548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-01DOI: 10.1016/s0885-064x(21)00078-9
Josef Dick, A. Hinrichs, E. Novak, K. Ritter, G. Wasilkowski, H. Wozniakowski
{"title":"Changes of the Editorial Board","authors":"Josef Dick, A. Hinrichs, E. Novak, K. Ritter, G. Wasilkowski, H. Wozniakowski","doi":"10.1016/s0885-064x(21)00078-9","DOIUrl":"https://doi.org/10.1016/s0885-064x(21)00078-9","url":null,"abstract":"","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"21 1","pages":"101623"},"PeriodicalIF":2.1,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87739957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the last years, vaccines debate has attracted the attention of all the social media, with an outstanding increase during COVID-19 vaccination campaigns. The topic has created at least two opposing factions, pro- and anti-vaccines, that have conflicting and incompatible narratives. However, a not negligible fraction of the population has an unclear position, as many citizens feel confused by the vast amount of information coming from both sides in the online social network. The engagement of the undecided population by the two parties has a key role in the success of the vaccination campaigns. In this article, we present three models used to describe the recruitment of the undecided population by pro-vax and no-vax factions in a three-states context. Starting from real-world data of Facebook pages previously labelled as pro-, anti-vaccines or neutral, we describe and compare three opinion dynamics models that catch different behaviours of the undecided population. The first one is a variation of the SIS model, where undecided position is considered an indifferent position, including users not interested in the discussion. Neutrals can be 'infected' by one of the two extreme factions, joining their side, and they 'recover' when they lose interest in the debate and go back to neutrality. The second model studied is a Voters model with three parties: neutral pages represent a centrist position. They lean on their original ideas, that are different from both the other parties. The last is the Bilingual model adapted to the vaccination debate: it describes a context where neutral individuals are in agreement with both pro- and anti-vax factions, with a position of compromise between the extremes ('bilingualism'). If they have a one-sided neighbourhood, the necessity (or the convenience) to agree with both parties comes out, and bi-linguists can become mono-linguists. Our results depicts an agreement between the three models: anti-vax opinion propagates more than pro-vax, thanks to an initial strategic position in the online social network (even if they start with a smaller population). While most of the pro-vaccines nodes are segregated in their own communities, no-vaccines ones are entangled at the core of the network, where the majority of the undecided population is located. In the last section, we propose and compare some policies that could be applied to the network to prevent anti-vax overcome: they lead us to conclude that censoring strategies are not effective, as well as segregating scenarios based on unfollowing decisions, while the addition of links in the network favours the containment of the pro-vax domain, reducing the distance between pro-vaxxers and undecided population.
{"title":"Ensemble of opinion dynamics models to understand the role of the undecided about vaccines","authors":"Jacopo Lenti, G. Ruffo","doi":"10.1093/comnet/cnac018","DOIUrl":"https://doi.org/10.1093/comnet/cnac018","url":null,"abstract":"In the last years, vaccines debate has attracted the attention of all the social media, with an outstanding increase during COVID-19 vaccination campaigns. The topic has created at least two opposing factions, pro- and anti-vaccines, that have conflicting and incompatible narratives. However, a not negligible fraction of the population has an unclear position, as many citizens feel confused by the vast amount of information coming from both sides in the online social network. The engagement of the undecided population by the two parties has a key role in the success of the vaccination campaigns. In this article, we present three models used to describe the recruitment of the undecided population by pro-vax and no-vax factions in a three-states context. Starting from real-world data of Facebook pages previously labelled as pro-, anti-vaccines or neutral, we describe and compare three opinion dynamics models that catch different behaviours of the undecided population. The first one is a variation of the SIS model, where undecided position is considered an indifferent position, including users not interested in the discussion. Neutrals can be 'infected' by one of the two extreme factions, joining their side, and they 'recover' when they lose interest in the debate and go back to neutrality. The second model studied is a Voters model with three parties: neutral pages represent a centrist position. They lean on their original ideas, that are different from both the other parties. The last is the Bilingual model adapted to the vaccination debate: it describes a context where neutral individuals are in agreement with both pro- and anti-vax factions, with a position of compromise between the extremes ('bilingualism'). If they have a one-sided neighbourhood, the necessity (or the convenience) to agree with both parties comes out, and bi-linguists can become mono-linguists. Our results depicts an agreement between the three models: anti-vax opinion propagates more than pro-vax, thanks to an initial strategic position in the online social network (even if they start with a smaller population). While most of the pro-vaccines nodes are segregated in their own communities, no-vaccines ones are entangled at the core of the network, where the majority of the undecided population is located. In the last section, we propose and compare some policies that could be applied to the network to prevent anti-vax overcome: they lead us to conclude that censoring strategies are not effective, as well as segregating scenarios based on unfollowing decisions, while the addition of links in the network favours the containment of the pro-vax domain, reducing the distance between pro-vaxxers and undecided population.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"10 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60891333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A novel Markovian network evolution model is introduced and analysed by means of information theory. It will be proved that the model, called network evolution chain, is a stationary and ergodic stochastic process. Therefore, the asymptotic equipartition property can be applied to it. The model’s entropy rate and typical sequences are also explored. Extracting particular information from the network and methods to simulate network evolution in the continuous time domain are discussed. Additionally, the Erdős–Rényi network evolution chain is introduced as a subset of our model with the additional property of its stationary distribution matching the Erdős–Rényi random graph model. The stationary distributions of nodes and graphs are calculated for this subset alongside its entropy rate. The simulation results at the end of the article back up the proved theorems and calculated values.
{"title":"An Information Theory Approach to Network Evolution Models","authors":"Amirmohammad Farzaneh, J. Coon","doi":"10.1093/comnet/cnac020","DOIUrl":"https://doi.org/10.1093/comnet/cnac020","url":null,"abstract":"\u0000 A novel Markovian network evolution model is introduced and analysed by means of information theory. It will be proved that the model, called network evolution chain, is a stationary and ergodic stochastic process. Therefore, the asymptotic equipartition property can be applied to it. The model’s entropy rate and typical sequences are also explored. Extracting particular information from the network and methods to simulate network evolution in the continuous time domain are discussed. Additionally, the Erdős–Rényi network evolution chain is introduced as a subset of our model with the additional property of its stationary distribution matching the Erdős–Rényi random graph model. The stationary distributions of nodes and graphs are calculated for this subset alongside its entropy rate. The simulation results at the end of the article back up the proved theorems and calculated values.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"37 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89780720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-18DOI: 10.1016/j.jco.2022.101691
Jiansong Li, Heping Wang
{"title":"Optimal randomized quadrature for weighted Sobolev and Besov classes with the Jacobi weight on the ball","authors":"Jiansong Li, Heping Wang","doi":"10.1016/j.jco.2022.101691","DOIUrl":"https://doi.org/10.1016/j.jco.2022.101691","url":null,"abstract":"","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"40 1","pages":"101691"},"PeriodicalIF":2.1,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85764277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the most effective strategies to mitigate the global spreading of a pandemic (e.g. coronavirus disease 2019) is to shut down international airports. From a network theory perspective, this is since international airports and flights, essentially playing the roles of bridge nodes and bridge links between countries as individual communities, dominate the epidemic spreading characteristics in the whole multi-community system. Among all epidemic characteristics, the peak fraction of infected, $I_{max}$, is a decisive factor in evaluating an epidemic strategy given limited capacity of medical resources but is seldom considered in multi-community models. In this article, we study a general two-community system interconnected by a fraction $r$ of bridge nodes and its dynamic properties, especially $I_{max}$, under the evolution of the susceptible-infected-recovered model. Comparing the characteristic time scales of different parts of the system allows us to analytically derive the asymptotic behaviour of $I_{max}$ with $r$, as $rrightarrow 0$, which follows different power-law relations in each regime of the phase diagram. We also detect crossovers when $I_{max}$ changes from one power law to another, crossing different power-law regimes as driven by $r$. Our results enable a better prediction of the effectiveness of strategies acting on bridge nodes, denoted by the power-law exponent $epsilon_I$ as in $I_{max}propto r^{1/epsilon_I}$.
{"title":"Peak fraction of infected in epidemic spreading for multi-community networks","authors":"Jing Ma, Xiangyi Meng, L. Braunstein","doi":"10.1093/comnet/cnac021","DOIUrl":"https://doi.org/10.1093/comnet/cnac021","url":null,"abstract":"\u0000 One of the most effective strategies to mitigate the global spreading of a pandemic (e.g. coronavirus disease 2019) is to shut down international airports. From a network theory perspective, this is since international airports and flights, essentially playing the roles of bridge nodes and bridge links between countries as individual communities, dominate the epidemic spreading characteristics in the whole multi-community system. Among all epidemic characteristics, the peak fraction of infected, $I_{max}$, is a decisive factor in evaluating an epidemic strategy given limited capacity of medical resources but is seldom considered in multi-community models. In this article, we study a general two-community system interconnected by a fraction $r$ of bridge nodes and its dynamic properties, especially $I_{max}$, under the evolution of the susceptible-infected-recovered model. Comparing the characteristic time scales of different parts of the system allows us to analytically derive the asymptotic behaviour of $I_{max}$ with $r$, as $rrightarrow 0$, which follows different power-law relations in each regime of the phase diagram. We also detect crossovers when $I_{max}$ changes from one power law to another, crossing different power-law regimes as driven by $r$. Our results enable a better prediction of the effectiveness of strategies acting on bridge nodes, denoted by the power-law exponent $epsilon_I$ as in $I_{max}propto r^{1/epsilon_I}$.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"128 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76205256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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