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TechRxiv 2020年

作者： Ye, Cong Slavakis, Konstantinos Nakuci, Johan Muldoon, Sarah F. Medaglia, John NY14260 United States State University of New York at Buffalo China The Neuroscience Program UB SUNY United States The Department of Mathematics and the Computational and Data-Enabled Science and Engineering Program UB SUNY United States The Department of Psychology Drexel University PA19104 United States The Perelman School of Medicine University of Pennsylvania PA19104 United States

This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. Kernel autoregressive-moving-average modeling and kernel (partial) correlations serve as case studies of generating features in the Riemannian manifolds of Grassmann and positive-(semi)definite matrices, respectively. Feature point-clouds form clusters which are viewed as submanifolds according to Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: feature points are first sampled in a non-random way to reveal the underlying geometric information, and, then, a fast sequential-clustering scheme is brought forth that takes advantage of Riemannian distances and the angular information of tangent spaces. By virtue of the landmark points and the sequential processing of the Riemannian features, the computational complexity of the framework is rendered free from the length of the available time-series data. The effectiveness and computational efficiency of the proposed framework are validated by extensive numerical tests against several state-of-the-art manifold-learning and brain-network-clustering schemes on synthetic as well as real functional-magnetic-resonance-imaging (fMRI) and electroencephalogram (EEG) data. © 2020, CC BY.

关键词： Time series

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Estimating international trade status of countries from global liner shipping networks

arXiv

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arXiv 2020年

作者： Xu, Mengqiao Pan, Qian Xia, Haoxiang Masuda, Naoki School of Economics and Management Dalian University of Technology No. 2 Linggong Road Ganjingzi District Dalian City Liaoning Province116024 China Department of Mathematics University at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-5030 United States

Maritime shipping is a backbone of international trade and thus the world economy. Vessels travel from a port of one country to another on networks of ports to carry cargos, which contribute to countries’ international trade values. We hypothesize that ports that involve transshipment activities serve as a third-party middleman to mediate trade between two foreign countries and contribute to the corresponding country's status in international trade. We test this hypothesis using a port-level data set of global liner shipping services. We propose two indices that quantify the importance of countries in the global liner shipping network and show that they explain a large amount of variation in the international trade value and related measures of individual countries. These results support a long-standing view in maritime economics, which has yet been directly tested, that countries that are strongly integrated into the global maritime transportation network have enhanced access to global markets and trade opportunities. Copyright © 2020, The Authors. All rights reserved.

关键词： International trade

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Interplay between k-core and community structure in complex networks

arXiv

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arXiv 2020年

作者： Malvestio, Irene Cardillo, Alessio Masuda, Naoki Department of Engineering Mathematics University of Bristol BristolBS8 1UB United Kingdom Department of Computer Science and Mathematics University Rovira i Virgili TarragonaE-43007 Spain University of Zaragoza ZaragozaE-50018 Spain Department of Mathematics University at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-5030 United States

The organisation of a network in a maximal set of nodes having at least k neighbours within the set, known as k-core decomposition, has been used for studying various phenomena. It has been shown that nodes in the innermost k-shells play a crucial role in contagion processes, emergence of consensus, and resilience of the system. It is known that the k-core decomposition of many empirical networks cannot be explained by the degree of each node alone, or equivalently, random graph models that preserve the degree of each node (i.e., configuration model). Here we study the k-core decomposition of some empirical networks as well as that of some randomised counterparts, and examine the extent to which the k-shell structure of the networks can be accounted for by the community structure. We find that preserving the community structure in the randomisation process is crucial for generating networks whose k-core decomposition is close to the empirical one. We also highlight the existence, in some networks, of a concentration of the nodes in the innermost k-shells into a small number of communities. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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SI-spreading-based network embedding in static and temporal networks

arXiv

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arXiv 2020年

作者： Zhan, Xiu-Xiu Li, Ziyu Masuda, Naoki Holme, Petter Wang, Huijuan Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands Department of Mathematics University at Buffalo State University of New York Buffalo New YorkNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York Buffalo New YorkNY14260-2900 United States Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan

Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low-dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task. Nodes with similar embedding vectors are more likely to be connected. Classic network embedding algorithms are random-walk-based. They sample trajectory paths via random walks and generate node pairs from the trajectory paths. The node pair set is further used as the input for a Skip-Gram model, a representative language model that embeds nodes (which are regarded as words) into vectors. In the present study, we propose to replace random walk processes by a spreading process, namely the susceptible-infected (SI) model, to sample paths. Specifically, we propose two SI-spreading-based algorithms, SINE and TSINE, to embed static and temporal networks, respectively. The performance of our algorithms is evaluated by the missing link prediction task in comparison with state-of-the-art static and temporal network embedding algorithms. Results show that SINE and TSINE outperform the baselines across all six empirical datasets. We further find that the performance of SINE is mostly better than TSINE, suggesting that temporal information does not necessarily improve the embedding for missing link prediction. Moreover, we study the effect of the sampling size, quantified as the total length of the trajectory paths, on the performance of the embedding algorithms. The better performance of SINE and TSINE requires a smaller sampling size in comparison with the baseline algorithms. Hence, SI-spreading-based embedding tends to be more applicable to large-scale networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Network embeddings

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Opinion dynamics on tie-decay networks

arXiv

引用

arXiv 2020年

作者： Sugishita, Kashin Porter, Mason A. Beguerisse-Díaz, Mariano Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Department of Mathematics University of California Los AngelesCA90095 United States Mathematical Institute University of Oxford OxfordOX2 6GG United Kingdom Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

In social networks, interaction patterns typically change over time. We study opinion dynamics on tie-decay networks in which tie strength increases instantaneously when there is an interaction and decays exponentially between interactions. Specifically, we formulate continuous-time Laplacian dynamics and a discrete-time DeGroot model of opinion dynamics on these tie-decay networks, and we carry out numerical computations for the continuous-time Laplacian dynamics. We examine the speed of convergence by studying the spectral gaps of combinatorial Laplacian matrices of tie-decay networks. First, we compare the spectral gaps of the Laplacian matrices of tie-decay networks that we construct from empirical data with the spectral gaps for corresponding randomized and aggregate networks. We find that the spectral gaps for the empirical networks tend to be smaller than those for the randomized and aggregate networks. Second, we study the spectral gap as a function of the tie-decay rate and time. Intuitively, we expect small tie-decay rates to lead to fast convergence because the influence of each interaction between two nodes lasts longer for smaller decay rates. Moreover, as time progresses and more interactions occur, we expect eventual convergence. However, we demonstrate that the spectral gap need not decrease monotonically with respect to the decay rate or increase monotonically with respect to time. Our results highlight the importance of the interplay between the times that edges strengthen and decay in temporal networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Laplace transforms

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Critical mass effect in evolutionary games triggered by zealots

引用

Physical Review Research 2020年第2期2卷 023305-023305页

作者： Alessio Cardillo Naoki Masuda Department of Engineering Mathematics University of Bristol Bristol BS8 1UB United Kingdom Department of Computer Science and Mathematics University Rovira i Virgili E-43007 Tarragona Spain GOTHAM Lab – Institute for Biocomputation and Physics of Complex Systems (BIFI) University of Zaragoza E-50018 Zaragoza Spain Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Tiny perturbations may trigger large responses in systems near criticality, shifting them across equilibria. Committed minorities are suggested to be responsible for the emergence of collective behaviors in many physical, social, and biological systems. Using evolutionary game theory, we address the question whether a finite fraction of zealots can drive the system to large-scale coordination. We find that a tipping point exists in coordination games, whereas the same phenomenon depends on the selection pressure, update rule, and network structure in other types of games. Our study paves the way to understand social systems driven by the individuals' benefit in the presence of zealots, such as human vaccination behavior or cooperative transports in animal groups.

关键词： Patterns in complex systems Social systems

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A numerical study of bayesian inference of surface catalycity in low speed reacting flow using laser absorption spectroscopy 12th

A numerical study of bayesian inference of surface catalycit...

引用

12th AIAA/ASME Joint Thermophysics and Heat Transfer Conference, 2018

作者： Adowski, Timothy R. Bauman, Paul T. Department of Mechanical and Aerospace Engineering University at Buffalo BuffaloNY14260-4400 United States Department of Mechanical and Aerospace Engineering Computational and Data-Enabled Science and Engineering University at Buffalo BuffaloNY14260-4400 United States

ISBN: (数字)9781624105524

ISBN: (纸本)9781624105524

In this work, we investigate the statistical inference of surface chemistry parameters from noisy data stemming from tunable diode laser absorption spectroscopy (TDLAS). We leverage previous work whereby TDLAS is simulated in two-dimensional finite element simulations of low speed reacting flow. In particular, we compute the attenuation of laser intensity along an optical path that passes through carbon monoxide CO gas generated by a reacting specimen. The goal is to be able to infer parameters related to the surface chemistry based on the laser attenuation data. Using baseline values from the literature, we numerically simulate TDLAS data and add statistical noise. We then use this synthetic data to infer parameters in various surface chemistry models to demonstrate the feasibility of the methodology. © 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： Surface chemistry

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Detecting anomalous citation groups in journal networks

arXiv

引用

arXiv 2020年

作者： Kojaku, Sadamori Livan, Giacomo Masuda, Naoki Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Computer Science University College London LondonWC1E 6EA United Kingdom Systemic Risk Centre London School of Economics and Political Science LondonWC2A 2AE United Kingdom Department of Mathematics University at Buffalo State University of New York BuffaloNY United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-2900 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

The ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher publication standards. However, the fixation on higher impact factors leads some journals to artificially boost impact factors through the coordinated effort of a "citation cartel" of journals. "Citation cartel" behavior has become increasingly common in recent years, with several instances being reported. Here, we propose an algorithm—-named CIDRE—to detect anomalous groups of journals that exchange citations at excessively high rates when compared against a null model that accounts for scientific communities and journal size. CIDRE detects more than half of the journals suspended from Journal Citation Reports due to anomalous citation behavior in the year of suspension or in advance. Furthermore, CIDRE detects many new anomalous groups, where the impact factors of the member journals are lifted substantially higher by the citations from other member journals. We describe a number of such examples in detail and discuss the implications of our findings with regard to the current academic climate. Copyright © 2020, The Authors. All rights reserved.

关键词： Publishing

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Skater’s dilemma

arXiv

引用

arXiv 2020年

作者： Ichinose, Genki Miyagawa, Daiki Ito, Junji Masuda, Naoki Department of Mathematical and Systems Engineering Shizuoka University 3-5-1 Johoku Naka-ku Hamamatsu432-8561 Japan Skating Club Yamanashi Gakuin University 2-4-5 Sakaori Kofu Yamanashi400-8575 Japan Department of Mathematics University at Buffalo State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-5030 United States

In some athletic races, such as cycling and types of speed skating races, athletes have to complete a relatively long distance at a high speed in the presence of direct opponents. To win such a race, athletes are motivated to hide behind others to suppress energy consumption before a final moment of the race. This situation seems to produce a social dilemma: players want to hide behind others, whereas if a group of players attempts to do so, they may all lose to other players that overtake them. To support that speed skaters are involved in such a social dilemma, we analyzed video footage data for 14 mass start skating races to find that skaters that hid behind others to avoid air resistance for a long time before the final lap tended to win. Furthermore, the finish rank of the skaters in mass start races was independent of the record of the same skaters in time-trial races measured in the absence of direct opponents. The results suggest that how to strategically cope with a skater’s dilemma may be a key determinant for winning long-distance and high-speed races with direct *** Codes 91A80 Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

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Brain-network clustering via kernel-ARMA modeling and the Grassmannian

arXiv

引用

arXiv 2019年

作者： Ye, Cong Slavakis, Konstantinos Patil, Pratik V. Muldoon, Sarah F. Medaglia, John NY14260 United States Department of Mathematics Computational and Data-Enabled Science and Engineering Program University at Buffalo SUNY NY14260 United States Department of Psychology Drexel University PA19104 United States Perelman School of Medicine University of Pennsylvania PA19104 United States

Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-network clustering via time-series analysis. Notwithstanding, most of the brain-network clustering methods revolve around state clustering and/or node clustering (a.k.a. community detection or topology inference) within states. This work answers first the need of capturing non-linear nodal dependencies by bringing forth a novel feature-extraction mechanism via kernel autoregressive-moving-average modeling. The extracted features are mapped to the Grassmann manifold (Grassmannian), which consists of all linear subspaces of a fixed rank. By virtue of the Riemannian geometry of the Grassmannian, a unifying clustering framework is offered to tackle all possible clustering problems in a network: Cluster multiple states, detect communities within states, and even identify/track subnetwork state sequences. The effectiveness of the proposed approach is underlined by extensive numerical tests on synthetic and real fMRI/EEG data which demonstrate that the advocated learning method compares favorably versus several state-of-the-art clustering schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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