Inference

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 …