Algorithmic systems are often called upon to assist in high-stakes decis...
Social media have great potential for enabling public discourse on impor...
Counterfactual examples have emerged as an effective approach to produce...
We present a new effective and scalable framework for training GNNs in
s...
We study point-to-point distance estimation in hypergraphs, where the qu...
The Densest Subgraph Problem requires to find, in a given graph, a subse...
Submodular function maximization is central in numerous data science
app...
Which messages are more effective at inducing a change of opinion in the...
This paper introduces a new type of causal structure, namely multiscale
...
Despite echo chambers in social media have been under considerable scrut...
While much attention has been devoted to the causes of opinion change, l...
Agent-Based Models (ABMs) are used in several fields to study the evolut...
Most methods for explaining black-box classifiers (e.g., on tabular data...
Recommender systems typically suggest to users content similar to what t...
Simplicial complexes are a generalization of graphs that model higher-or...
Graph summarization is beneficial in a wide range of applications, such ...
Dense subgraph discovery is a fundamental problem in graph mining with a...
In recent years, multi-factor strategies have gained increasing populari...
Modeling information cascades in a social network through the lenses of ...
We study a novel problem of fairness in ranking aimed at minimizing the
...
Training graph classifiers able to distinguish between healthy brains an...
Given a set of vertices in a network, that we believe are of interest fo...
Community search is a well-studied problem which, given a static graph a...
Dense subgraph discovery is an important graph-mining primitive with a
v...
Opinion dynamics - the research field dealing with how people's opinions...
We study the emergence of support for Donald Trump in Reddit's political...
The rapid dynamics of COVID-19 calls for quick and effective tracking of...
Correlation clustering is arguably the most natural formulation of
clust...
Signed networks contain edge annotations to indicate whether each intera...
When analyzing temporal networks, a fundamental task is the identificati...
The k-core of a graph is defined as the maximal subgraph in which every
...
Multilayer networks are a powerful paradigm to model complex systems, wh...
When analyzing temporal networks, a fundamental task is the identificati...
In this paper we study the problem of discovering a timeline of events i...
Mastering the dynamics of social influence requires separating, in a dat...
In discrete search and optimization problems where the elements that may...
Discrimination discovery from data is an important task aiming at identi...