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As more and more network-structured data sets are available, the statistical analysis of valued graphs has become common place. Looking for a latent structure is one of the many strategies used to better understand the behavior of a…

Applications · Statistics 2010-11-09 Mahendra Mariadassou , Stéphane Robin , Corinne Vacher

Recent research shows that humans are heavily influenced by online social interactions: We are more likely to perform actions which, in the past, have led to positive social feedback. We introduce a quantitative model of behavior changes in…

Social and Information Networks · Computer Science 2014-07-01 Sanmay Das , Allen Lavoie

A graphical model is a structured representation of the data generating process. The traditional method to reason over random variables is to perform inference in this graphical model. However, in many cases the generating process is only a…

Machine Learning · Statistics 2019-10-31 Victor Garcia Satorras , Zeynep Akata , Max Welling

Graphical models are widely used in diverse application domains to model the conditional dependencies amongst a collection of random variables. In this paper, we consider settings where the graph structure is covariate-dependent, and…

Machine Learning · Statistics 2025-04-24 Jiahe Lin , Yikai Zhang , George Michailidis

We introduce a novel class of graphical models, termed profile graphical models, that represent, within a single graph, how an external factor influences the dependence structure of a multivariate set of variables. This class is quite…

Methodology · Statistics 2026-03-31 Alejandra Avalos-Pacheco , Monia Lupparelli , Francesco C. Stingo

The Internet is a complex network of interconnected routers and the existence of collective behavior such as congestion suggests that the correlations between different connections play a crucial role. It is thus critical to measure and…

Disordered Systems and Neural Networks · Physics 2009-11-07 Marc Barthelemy , Bernard Gondran , Eric Guichard

Traditional survival analysis techniques focus on the occurrence of failures over the time. During analysis of such events, ignoring the related unobserved covariates or heterogeneity involved in data sample may leads us to adverse…

Methodology · Statistics 2021-12-22 Shikhar Tyagi , Arvind Pandey , David D Hanagal

Multivariate geostatistics is based on modelling all covariances between all possible combinations of two or more variables at any sets of locations in a continuously indexed domain. Multivariate spatial covariance models need to be built…

Methodology · Statistics 2016-10-10 Noel Cressie , Andrew Zammit-Mangion

Financial markets are a classical example of complex systems as they comprise many interacting stocks. As such, we can obtain a surprisingly good description of their structure by making the rough simplification of binary daily returns.…

Statistical Finance · Quantitative Finance 2014-01-28 Thomas Bury

Understanding how people interact with the web is key for a variety of applications, e.g., from the design of effective web pages to the definition of successful online marketing campaigns. Browsing behavior has been traditionally…

Computers and Society · Computer Science 2021-05-05 Luca Vassio , Idilio Drago , Marco Mellia , Zied Ben Houidi , Mohamed Lamine Lamali

Online popularity has enormous impact on opinions, culture, policy, and profits. We provide a quantitative, large scale, temporal analysis of the dynamics of online content popularity in two massive model systems, the Wikipedia and an…

The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from…

Statistics Theory · Mathematics 2016-08-16 Nicolai Meinshausen , Peter Bühlmann

A statistical model assuming a preferential attachment network, which is generated by adding nodes sequentially according to a few simple rules, usually describes real-life networks better than a model assuming, for example, a Bernoulli…

Computation · Statistics 2018-10-01 Clement Lee , Andrew Garbett , Darren J. Wilkinson

Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional dependencies between the variables. We…

Methodology · Statistics 2010-04-05 Christophe Ambroise , Julien Chiquet , Catherine Matias

We propose a procedure for sparse regression with pairwise interactions, by generalizing the Univariate Guided Sparse Regression (UniLasso) methodology. A central contribution is our introduction of a concept of univariate (or marginal)…

Methodology · Statistics 2026-01-05 Aymen Echarghaoui , Robert Tibshirani

With the advancement of information technology, more people, especially young adults, are getting addicted to the use of different social media platforms. Despite immense useful applications in communication and interactions, the habit of…

Physics and Society · Physics 2023-07-20 Dibyajyoti Mallick , Priya Chakraborty , Sayantari Ghosh

Modeling Internet growth is important both for understanding the current network and to predict and improve its future. To date, Internet models have typically attempted to explain a subset of the following characteristics: network…

Networking and Internet Architecture · Computer Science 2008-06-25 Petter Holme , Josh Karlin , Stephanie Forrest

Infographic is a type of information presentation that inbound marketers use. I suggest a method that can allow the infographic designers to benchmark their design against the previous viral infographics to measure whether a given design…

Human-Computer Interaction · Computer Science 2016-11-04 Meisam Hejazi Nia

How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for…

Social and Information Networks · Computer Science 2014-06-24 Flavio Figueiredo , Jussara M. Almeida , Yasuko Matsubara , Bruno Ribeiro , Christos Faloutsos

A semi-parametric, non-linear regression model in the presence of latent variables is applied towards learning network graph structure. These latent variables can correspond to unmodeled phenomena or unmeasured agents in a complex system of…

Machine Learning · Statistics 2018-07-03 Jonathan Mei , José M. F. Moura
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