Inference

Inference on networks

Complex interacting systems are often represented by large datasets containing a considerable amount of information. The question is how to capture the relevant macroscopic behavior by retaining only a small amount of information.

Community detection with node attributes in multilayer networks

Community detection in networks is commonly performed using information about interactions between nodes. Recent advances have been made to incorporate multiple types of interactions, thus generalising standard methods to multilayer networks. Often …

Sampling on networks: estimating spectral centrality measures and their impact in evaluating other relevant network measures

We perform an extensive analysis of how sampling impacts the estimate of several relevant network measures. In particular, we focus on how a sampling strategy optimized to recover a particular spectral centrality measure impacts other topological …

Sampling on Networks: Estimating Eigenvector Centrality on Incomplete Networks

We develop a new sampling method to estimate eigenvector centrality on incomplete networks. Our goalis to estimate this global centrality measure having at disposal a limited amount of data. This is the case inmany real-world scenarios where data …

A physical model for efficient ranking in networks

We present a physically-inspired model and an efficient algorithm to infer hierarchical rankings of nodes in directed networks. It assigns real-valued ranks to nodes rather than simply ordinal ranks, and it formalizes the assumption that interactions …

Are `Water Smart Landscapes' Contagious? An epidemic approach on networks to study peer effects

We test the existence of a neighborhood based peer effect around participation in an incentive based conservation program called `Water Smart Landscapes' (WSL) in the city of Las Vegas, Nevada. We use 15 years of geo-coded daily records of WSL …

Community detection, link prediction, and layer interdependence in multilayer networks

Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated …