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  1. Article ; Online: Spatial interactions in urban scaling laws.

    Altmann, Eduardo G

    PloS one

    2020  Volume 15, Issue 12, Page(s) e0243390

    Abstract: Analyses of urban scaling laws assume that observations in different cities are independent of the existence of nearby cities. Here we introduce generative models and data-analysis methods that overcome this limitation by modelling explicitly the effect ... ...

    Abstract Analyses of urban scaling laws assume that observations in different cities are independent of the existence of nearby cities. Here we introduce generative models and data-analysis methods that overcome this limitation by modelling explicitly the effect of interactions between individuals at different locations. Parameters that describe the scaling law and the spatial interactions are inferred from data simultaneously, allowing for rigorous (Bayesian) model comparison and overcoming the problem of defining the boundaries of urban regions. Results in five different datasets show that including spatial interactions typically leads to better models and a change in the exponent of the scaling law.
    MeSH term(s) Brazil ; Cities/economics ; City Planning/economics ; Computer Simulation ; Data Interpretation, Statistical ; Humans ; Probability
    Language English
    Publishing date 2020-12-07
    Publishing country United States
    Document type Journal Article
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0243390
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  2. Book ; Online: Quantifying the Dissimilarity of Texts

    Shade, Benjamin / Altmann, Eduardo G.

    2023  

    Abstract: Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering. In this paper, we compared the properties and ... ...

    Abstract Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering. In this paper, we compared the properties and performance of different dissimilarity measures $D$ using three different representations of texts -- vocabularies, word frequency distributions, and vector embeddings -- and three simple tasks -- clustering texts by author, subject, and time period. Using the Project Gutenberg database, we found that the generalised Jensen--Shannon divergence applied to word frequencies performed strongly across all tasks, that $D$'s based on vector embedding representations led to stronger performance for smaller texts, and that the optimal choice of approach was ultimately task-dependent. We also investigated, both analytically and numerically, the behaviour of the different $D$'s when the two texts varied in length by a factor $h$. We demonstrated that the (natural) estimator of the Jaccard distance between vocabularies was inconsistent and computed explicitly the $h$-dependency of the bias of the estimator of the generalised Jensen--Shannon divergence applied to word frequencies. We also found numerically that the Jensen--Shannon divergence and embedding-based approaches were robust to changes in $h$, while the Jaccard distance was not.

    Comment: 16 pages, 4 figures, part of the Special Issue Novel Methods and Applications in Natural Language Processing
    Keywords Computer Science - Computation and Language ; Condensed Matter - Statistical Mechanics ; Physics - Physics and Society
    Subject code 410
    Publishing date 2023-05-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Sampling triangulations of manifolds using Monte Carlo methods

    Altmann, Eduardo G. / Spreer, Jonathan

    2023  

    Abstract: We propose a Monte Carlo method to efficiently find, count, and sample abstract triangulations of a given manifold M. The method is based on a biased random walk through all possible triangulations of M (in the Pachner graph), constructed by combining ( ... ...

    Abstract We propose a Monte Carlo method to efficiently find, count, and sample abstract triangulations of a given manifold M. The method is based on a biased random walk through all possible triangulations of M (in the Pachner graph), constructed by combining (bi-stellar) moves with suitable chosen accept/reject probabilities (Metropolis-Hastings). Asymptotically, the method guarantees that samples of triangulations are drawn at random from a chosen probability. This enables us not only to sample (rare) triangulations of particular interest but also to estimate the (extremely small) probability of obtaining them when isomorphism types of triangulations are sampled uniformly at random. We implement our general method for surface triangulations and 1-vertex triangulations of 3-manifolds. To showcase its usefulness, we present a number of experiments: (a) we recover asymptotic growth rates for the number of isomorphism types of simplicial triangulations of the 2-dimensional sphere; (b) we experimentally observe that the growth rate for the number of isomorphism types of 1-vertex triangulations of the 3-dimensional sphere appears to be singly exponential in the number of their tetrahedra; and (c) we present experimental evidence that a randomly chosen isomorphism type of 1-vertex n-tetrahedra 3-sphere triangulation, for n tending to infinity, almost surely shows a fixed edge-degree distribution which decays exponentially for large degrees, but shows non-monotonic behaviour for small degrees.

    Comment: 29 pages, 6 figures
    Keywords Mathematics - Combinatorics ; Condensed Matter - Statistical Mechanics ; Computer Science - Computational Geometry ; Mathematics - Geometric Topology ; Physics - Computational Physics ; 57Q15 ; 60J10 ; 57-08
    Subject code 519
    Publishing date 2023-10-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Probabilistic description of dissipative chaotic scattering.

    Burton, Lachlan G / Dullin, Holger R / Altmann, Eduardo G

    Physical review. E

    2023  Volume 108, Issue 5-1, Page(s) 54223

    Abstract: We investigate the extent to which the probabilistic properties of chaotic scattering systems with dissipation can be understood from the properties of the dissipation-free system. For large energies, a fully chaotic scattering leads to an exponential ... ...

    Abstract We investigate the extent to which the probabilistic properties of chaotic scattering systems with dissipation can be understood from the properties of the dissipation-free system. For large energies, a fully chaotic scattering leads to an exponential decay of the survival probability P(t)∼e^{-κt}, with an escape rate κ that decreases with energy. Dissipation leads to the appearance of different finite-time regimes in P(t). We show how these different regimes can be understood for small dissipations and long times from the (effective) escape rate κ (including the nonhyperbolic regime) of the conservative system, until the energy reaches a critical value at which no escape is possible. More generally, we argue that for small dissipation and long times the surviving trajectories in the dissipative system are distributed according to the conditionally invariant measure of the conservative system at the corresponding energy. Quantitative predictions of our general theory are compared with numerical simulations in the Hénon-Heiles model.
    Language English
    Publishing date 2023-12-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2844562-4
    ISSN 2470-0053 ; 2470-0045
    ISSN (online) 2470-0053
    ISSN 2470-0045
    DOI 10.1103/PhysRevE.108.054223
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Nonassortative relationships between groups of nodes are typical in complex networks.

    Liu, Cathy Xuanchi / Alexander, Tristram J / Altmann, Eduardo G

    PNAS nexus

    2023  Volume 2, Issue 11, Page(s) pgad364

    Abstract: Decomposing a graph into groups of nodes that share similar connectivity properties is essential to understand the organization and function of complex networks. Previous works have focused on groups with specific relationships between group members, ... ...

    Abstract Decomposing a graph into groups of nodes that share similar connectivity properties is essential to understand the organization and function of complex networks. Previous works have focused on groups with specific relationships between group members, such as assortative communities or core-periphery structures, developing computational methods to find these mesoscale structures within a network. Here, we go beyond these two traditional cases and introduce a methodology that is able to identify and systematically classify all possible community types in directed multi graphs, based on the pairwise relationship between groups. We apply our approach to 53 different networks and find that assortative communities are the most common structures, but that previously unexplored types appear in almost every network. A particularly prevalent new type of relationship, which we call a
    Language English
    Publishing date 2023-11-06
    Publishing country England
    Document type Journal Article
    ISSN 2752-6542
    ISSN (online) 2752-6542
    DOI 10.1093/pnasnexus/pgad364
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  6. Article ; Online: Modelling daily weight variation in honey bee hives.

    Arias-Calluari, Karina / Colin, Theotime / Latty, Tanya / Myerscough, Mary / Altmann, Eduardo G

    PLoS computational biology

    2023  Volume 19, Issue 3, Page(s) e1010880

    Abstract: A quantitative understanding of the dynamics of bee colonies is important to support global efforts to improve bee health and enhance pollination services. Traditional approaches focus either on theoretical models or data-centred statistical analyses. ... ...

    Abstract A quantitative understanding of the dynamics of bee colonies is important to support global efforts to improve bee health and enhance pollination services. Traditional approaches focus either on theoretical models or data-centred statistical analyses. Here we argue that the combination of these two approaches is essential to obtain interpretable information on the state of bee colonies and show how this can be achieved in the case of time series of intra-day weight variation. We model how the foraging and food processing activities of bees affect global hive weight through a set of ordinary differential equations and show how to estimate the parameters of this model from measurements on a single day. Our analysis of 10 hives at different times shows that the estimation of crucial indicators of the health of honey bee colonies are statistically reliable and fall in ranges compatible with previously reported results. The crucial indicators, which include the amount of food collected (foraging success) and the number of active foragers, may be used to develop early warning indicators of colony failure.
    MeSH term(s) Bees ; Animals ; Data Interpretation, Statistical ; Food ; Pollination ; Time Factors ; Urticaria
    Language English
    Publishing date 2023-03-01
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1010880
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  7. Article ; Online: Testing Statistical Laws in Complex Systems.

    Gerlach, Martin / Altmann, Eduardo G

    Physical review letters

    2019  Volume 122, Issue 16, Page(s) 168301

    Abstract: The availability of large datasets requires an improved view on statistical laws in complex systems, such as Zipf's law of word frequencies, the Gutenberg-Richter law of earthquake magnitudes, or scale-free degree distribution in networks. In this Letter, ...

    Abstract The availability of large datasets requires an improved view on statistical laws in complex systems, such as Zipf's law of word frequencies, the Gutenberg-Richter law of earthquake magnitudes, or scale-free degree distribution in networks. In this Letter, we discuss how the statistical analysis of these laws are affected by correlations present in the observations, the typical scenario for data from complex systems. We first show how standard maximum-likelihood recipes lead to false rejections of statistical laws in the presence of correlations. We then propose a conservative method (based on shuffling and undersampling the data) to test statistical laws and find that accounting for correlations leads to smaller rejection rates and larger confidence intervals on estimated parameters.
    Language English
    Publishing date 2019-05-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.122.168301
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    In stock of ZB MED Bonn / Germany

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  8. Book ; Online: Non-parametric power-law surrogates

    Moore, Jack Murdoch / Yan, Gang / Altmann, Eduardo G.

    2022  

    Abstract: Power-law distributions are essential in computational and statistical investigations of extreme events and complex systems. The usual technique to generate power-law distributed data is to first infer the scale exponent $\alpha$ using the observed data ... ...

    Abstract Power-law distributions are essential in computational and statistical investigations of extreme events and complex systems. The usual technique to generate power-law distributed data is to first infer the scale exponent $\alpha$ using the observed data of interest and then sample from the associated distribution. This approach has important limitations because it relies on a fixed $\alpha$ (e.g., it has limited applicability in testing the {\it family} of power-law distributions) and on the hypothesis of independent observations (e.g., it ignores temporal correlations and other constraints typically present in complex systems data). Here we propose a constrained surrogate method that overcomes these limitations by choosing uniformly at random from a set of sequences exactly as likely to be observed under a discrete power-law as the original sequence (i.e., regardless of $\alpha$) and by showing how additional constraints can be imposed in the sequence (e.g., the Markov transition probability between states). This non-parametric approach involves redistributing observed prime factors to randomize values in accordance with a power-law model but without restricting ourselves to independent observations or to a particular $\alpha$. We test our results in simulated and real data, ranging from the intensity of earthquakes to the number of fatalities in disasters.

    Comment: 16 pages, 12 figures (main manuscript); 8 pages, 7 figures (supplemental material); For code, see https://github.com/JackMurdochMoore/power-law
    Keywords Nonlinear Sciences - Adaptation and Self-Organizing Systems
    Subject code 612
    Publishing date 2022-04-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Unraveling the Origin of Social Bursts in Collective Attention.

    De Domenico, Manlio / Altmann, Eduardo G

    Scientific reports

    2020  Volume 10, Issue 1, Page(s) 4629

    Abstract: In the era of social media, every day billions of individuals produce content in socio-technical systems resulting in a deluge of information. However, human attention is a limited resource and it is increasingly challenging to consume the most suitable ... ...

    Abstract In the era of social media, every day billions of individuals produce content in socio-technical systems resulting in a deluge of information. However, human attention is a limited resource and it is increasingly challenging to consume the most suitable content for one's interests. In fact, the complex interplay between individual and social activities in social systems overwhelmed by information results in bursty activity of collective attention which are still poorly understood. Here, we tackle this challenge by analyzing the online activity of millions of users in a popular microblogging platform during exceptional events, from NBA Finals to the elections of Pope Francis and the discovery of gravitational waves. We observe extreme fluctuations in collective attention that we are able to characterize and explain by considering the co-occurrence of two fundamental factors: the heterogeneity of social interactions and the preferential attention towards influential users. Our findings demonstrate how combining simple mechanisms provides a route towards understanding complex social phenomena.
    MeSH term(s) Algorithms ; Attention ; Humans ; Online Social Networking ; Social Behavior ; Social Interaction ; Social Media
    Language English
    Publishing date 2020-03-13
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-020-61523-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Micro-, meso-, macroscales: The effect of triangles on communities in networks.

    Wharrie, Sophie / Azizi, Lamiae / Altmann, Eduardo G

    Physical review. E

    2019  Volume 100, Issue 2-1, Page(s) 22315

    Abstract: Mesoscale structures (communities) are used to understand the macroscale properties of complex networks, such as their functionality and formation mechanisms. Microscale structures are known to exist in most complex networks (e.g., large number of ... ...

    Abstract Mesoscale structures (communities) are used to understand the macroscale properties of complex networks, such as their functionality and formation mechanisms. Microscale structures are known to exist in most complex networks (e.g., large number of triangles or motifs), but they are absent in the simple random-graph models considered (e.g., as null models) in community-detection algorithms. In this paper we investigate the effect of microstructures on the appearance of communities in networks. We find that alone the presence of triangles leads to the appearance of communities even in methods designed to avoid the detection of communities in random networks. This shows that communities can emerge spontaneously from simple processes of motiff generation happening at a microlevel. Our results are based on four widely used community-detection approaches (stochastic block model, spectral method, modularity maximization, and the Infomap algorithm) and three different generative network models (triadic closure, generalized configuration model, and random graphs with triangles).
    Language English
    Publishing date 2019-09-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2844562-4
    ISSN 2470-0053 ; 2470-0045
    ISSN (online) 2470-0053
    ISSN 2470-0045
    DOI 10.1103/PhysRevE.100.022315
    Database MEDical Literature Analysis and Retrieval System OnLINE

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