Article
Physics, Multidisciplinary
Federico Musciotto, Federico Battiston, Rosario N. Mantegna
Summary: The study proposes a method for detecting informative connections of any order in statistically validated hypergraphs, showing that hyperlinks are more informative than traditional pairwise approaches when applied to synthetic and real-world systems. Interactions in many real-world systems are often not limited to dyads, but involve three or more agents at a time, better described by hypergraphs encoding higher-order interactions among a group of nodes.
COMMUNICATIONS PHYSICS
(2021)
Article
Statistics & Probability
Paolo Pagnottoni, Alessandro Spelta
Summary: This paper proposes a method for characterizing the local structure of weighted multivariate time series networks and demonstrates its usefulness with an application to global commodity prices. The study finds that complex triadic structures become more important after the Global Financial Crisis, while all types of subgraphs become more coherent.
STATISTICAL METHODS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Andrea Simonetti, Alessandro Albano, Antonella Plaia, Michele Tumminello
Summary: Probabilistic topic models are popular in textual analysis, but their interpretability is often an issue. Evaluating topic coherence automatically has been a focus of research, and this article presents a new evaluation method based on statistically validated networks (SVNs). By representing topics as weighted networks of probable words and statistically validating co-occurrences, the method distinguishes between high and low-quality topics. The proposed measure, based on semantic coherence and interpretability, outperforms state-of-the-art coherence measures according to human judgment.
JOURNAL OF INFORMATION SCIENCE
(2023)
Article
Physics, Multidisciplinary
Christian Bongiorno, Salvatore Micciche, Rosario N. Mantegna
Summary: We develop a fast and scalable algorithm for detecting a nested partition from a dendrogram obtained from hierarchical clustering. Our algorithm provides a p-value for each clade in the hierarchical tree. We compare our results with those of the Pvclust algorithm and demonstrate that our algorithm is faster and more scalable. We also apply our algorithm to two empirical datasets and show that the clusters detected are meaningful.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Business, Finance
Michele Tumminello, Andrea Consiglio, Pietro Vassallo, Riccardo Cesari, Fabio Farabullini
Summary: This study develops an investigative system based on bipartite networks to improve fraud detection accuracy. Filtering rules are formalized through probability models and specific methods are tested to assess the existence of communities in extensive networks, providing new alert metrics for suspicious structures.
JOURNAL OF RISK AND INSURANCE
(2023)
Article
Mechanics
Ting Zhang, Kun Zhang, Laishui Lv, Xun Li, Yue Fang
Summary: This paper introduces a novel link prediction method based on non-negative tensor factorization that improves the performance of link prediction in temporal directed networks by effectively considering link direction and temporal information.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Mechanics
S. L. Tang, R. A. Antonia, L. Djenidi
Summary: In this study, we analyze the approach towards local isotropy in statistically stationary turbulent shear flows using the transport equations for the fourth-order moments of the velocity derivative. It is found that, as the Taylor microscale Reynolds number increases, the large-scale contribution gradually decreases and the small-scale motion becomes more locally isotropic. The rate at which local isotropy is approached depends on the weakening of the large-scale forcing, which is controlled by the magnitude of the non-dimensional velocity shear parameter.
JOURNAL OF FLUID MECHANICS
(2022)
Article
Biology
David P. Shorten, Viola Priesemann, Michael Wibral, Joseph T. Lizier, Tatyana O. Sharpee
Summary: This study quantifies the changes in information flow during neural development by analyzing the spontaneous activity of developing dissociated neural cell cultures. It reveals a dramatic increase in information flow quantity across networks during development, as well as the tendency for information flows to lock-in at specific points. Additionally, it characterizes the specialized computational roles undertaken by nodes during population bursts, with these roles aligning with average spike ordering and becoming regularly locked-in once established.
Article
Green & Sustainable Science & Technology
Nawhath Thanvisitthpon
Summary: This study proposed updated homestay indicators for Thai homestay businesses, validated by exploratory factor analysis and confirmatory factor analysis using structural equation modeling. It emphasized the importance of prioritizing components and indicators with high factor loadings for homestay operators.
Article
Computer Science, Artificial Intelligence
Yansong Wang, Xiaomeng Wang, Yijun Ran, Radoslaw Michalski, Tao Jia
Summary: One important task in the study of information cascade is to predict the future recipients of a message given its past spreading trajectory. The proposed method CasSeqGCN combines the network structure and temporal feature to accurately predict the future cascade size. The experiment demonstrates that the improved prediction comes from the design of the input and the GCN layer.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Optics
Mehdi Mabed, Lauri Salmela, Andrei Ermolaev, Christophe Finot, Goery Genty, John M. Dudley
Summary: We applied a neural network to correlate spectral and temporal properties of modulation instability excited by a continuous wave field with random quantum noise. The neural network showed excellent correlation with correlation coefficients exceeding rho = 0.90 and dynamic ranges exceeding 40 dB. The correlation was observed in both the initial evolving phase and the stationary phase of modulation instability, with or without distinct sideband structure. We also tested the ability of the neural network to forecast temporal instability peaks based on spectral intensity analysis at an earlier spatial reference position, but found it to be successful only within a very localized distance range.
OPTICS COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Nabeela Awan, Ahmad Ali, Fazlullah Khan, Muhammad Zakarya, Ryan Alturki, Mahwish Kundi, Mohammad Dahman Alshehri, Muhammad Haleem
Summary: This research proposes a novel spatio-temporal deep hybrid neural network for predicting citywide crowded traffic flows, utilizing different branches and neural network units to depict the spatio-temporal features of traffic flows, achieving competitive performance with existing prediction baselines.
Article
Automation & Control Systems
Zhongke Gao, Linhua Hou, Weidong Dang, Xinmin Wang, Xiaolin Hong, Xiong Yang, Guanrong Chen
Summary: In this study, a novel deep learning based soft measure technique was developed to predict the gas void fraction in gas-liquid two-phase flow. A multitask-based temporal-channelwise convolutional neural network was designed to extract features and predict the gas void fraction, showing superior performance compared to other competitive methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Aerospace
Bryan E. Schmidt, Wayne E. Page, Jeffrey A. Sutton
Summary: This work discusses the application of inverse Abel transformation in statistically axisymmetric data and explores its limitations. It is found that only the planar mean can be properly recovered for statistically axisymmetric but asymmetric data, while higher-order moments and one-sided Fourier spectrum cannot be recovered.
Article
Computer Science, Information Systems
Qihang Zhao, Yuzhe Zhang, Xiaodong Feng
Summary: In this paper, a novel deep learning framework called CasTCN is proposed for information cascade prediction. The framework can effectively capture the dynamic structure of information cascades and achieves better performance and efficiency compared to other baseline methods.
INFORMATION SYSTEMS
(2022)