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The Bethe Hessian and Information Theoretic Approaches for Online Change-Point Detection in Network Data
Sequences of networks are currently a common form of network data sets. Identification of structural change-points in a network data sequence is a...
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An attribute-based Node2Vec model for dynamic community detection on co-authorship network
Networks offer a wide range of applications in various domains of life and scientific research. Community detection, which aims at understanding the...
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Statistically validated coeherence and intensity in temporal networks of information flows
We propose a method for characterizing the local structure of weighted multivariate time series networks. We draw intensity and coherence of network...
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Byzantine-resilient decentralized network learning
Decentralized federated learning based on fully normal nodes has drawn attention in modern statistical learning. However, due to data corruption,...
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On the Randić index and its variants of network data
Summary statistics play an important role in network data analysis. They can provide us with meaningful insight into the structure of a network. The...
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DeepBiome: A Phylogenetic Tree Informed Deep Neural Network for Microbiome Data Analysis
Evidence linking the microbiome to human health is rapidly growing. The microbiome profile has the potential as a novel predictive biomarker for many...
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Clustering by deep latent position model with graph convolutional network
With the significant increase of interactions between individuals through numeric means, clustering of nodes in graphs has become a fundamental...
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High-cardinality categorical covariates in network regressions
High-cardinality (nominal) categorical covariates are challenging in regression modeling, because they lead to high-dimensional models. For example,...
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Overlap** coefficient in network-based semi-supervised clustering
Network-based Semi-Supervised Clustering (NeSSC) is a semi-supervised approach for clustering in the presence of an outcome variable. It uses a...
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Stacking-based neural network for nonlinear time series analysis
Stacked generalization is a commonly used technique for improving predictive accuracy by combining less expressive models using a high-level model....
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Moderate deviation principle of modularity in network
In the present paper, we study a specific partition of a given network and establish the moderate deviation principle of modularity for the partition...
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Network and attribute-based clustering of tennis players and tournaments
This paper aims at targeting some relevant issues for clustering tennis players and tournaments: (i) it considers players, tournaments and the...
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Design of Agricultural Field Experiments Accounting for both Complex Blocking Structures and Network Effects
We propose a novel model-based approach for constructing optimal designs with complex blocking structures and network effects for application in...
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Network-Based Discriminant Analysis for Multiclassification
Classification for multi-label responses, known as multiclassification, has been an important problem in supervised learning and has attracted our...
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A probabilistic method for reconstructing the Foreign Direct Investments network in search of ultimate host economies
The Ultimate Host Economies (UHEs) of a given country are defined as the ultimate destinations of Foreign Direct Investment (FDI) originating in that...
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Forest construction of Gaussian and discrete variables with the application of Watanabe Bayesian Information Criterion
This paper introduces a technique to estimate mutual information in data sets that comprise discrete and continuous variables. Utilizing the Chow–Liu...
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Network-Based Dimensionality Reduction for Textual Datasets
There is an increasing interest in develo** statistical tools for extracting information from textual datasets. In a text mining framework, a...
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Predictive Root Based Bootstrap Prediction Intervals in Neural Network Models for Time Series Forecasting
Time series (TS) modelling is an important area in the domain of statistics, as it enables us to comprehend the dynamics underlying a particular...
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A dynamic network model to measure exposure concentration in the Austrian interbank market
Motivated by an original financial network dataset, we develop a statistical methodology to study non-negatively weighted temporal networks. We focus...
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Estimating regional unemployment with mobile network data for Functional Urban Areas in Germany
The ongoing growth of cities due to better job opportunities is leading to increased labour-related commuter flows in several countries. On the one...
