Recommender systems typically suggest to users content similar to what they consumed in
the past. If a user happens to be exposed to strongly polarized content, she might …

Centrality metrics have become a popular concept in network science and optimization.
Over the years, centrality has been used to assign importance and identify influential …

The topology of the hyperlink graph among pages expressing different opinions may
influence the exposure of readers to diverse content. Structural bias may trap a reader in …

Structural bias or segregation of networks refers to situations where two or more disparate
groups are present in the network, so that the groups are highly connected internally, but …

In social networks, dense relationships among users contribute to stable communities.
Breakdowns of critical connections may cause users to leave the group. A popular model to …

The process of opinion formation is inherently a network process, with user opinions in a
social network being driven to a certain average opinion. One simple and intuitive …

The sets of hyperlinks in web pages, relationship ties in social networks, or sets of
recommendations in recommender systems, have a major impact on the diversity of content …

Most media content consumed today is provided by digital platforms that aggregate input
from diverse sources, where access to information is mediated by recommendation …

The identification of the set of k most central nodes of a graph, or centrality maximization, is a
key task in network analysis, with various applications ranging from finding communities in …

Node similarity measures quantify how similar a pair of nodes are in a network. These
similarity measures turn out to be an important fundamental tool for many real world …