280 citations · 777 across the 29 of their papers we have counts for
35 papers
Beyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark
Steve Azzolin, Francesco Paolo Nerini, Stefano Teso +4
Explainers for Graph Neural Networks (GNNs) are commonly evaluated by their plausibility, i.e., how well their explanations recover a predefined ground truth, such as a motif plant…
Inductive Correlation Clustering with Graph Neural Networks
Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan +1
Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and does not require a pre-specifie…
The Geometry of Representational Failures in Vision Language Models
Daniele Savietto, Declan Campbell, André Panisson +4
Vision-Language Models (VLMs) exhibit puzzling failures in multi-object visual tasks, such as hallucinating non-existent elements or failing to identify the most similar objects am…
Size-adaptive Hypothesis Testing for Fairness
Antonio Ferrara, Francesco Cozzi, Alan Perotti +2
Determining whether an algorithmic decision-making system discriminates against a specific demographic typically involves comparing a single point estimate of a fairness metric aga…
Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks
Francesco Cozzi, Marco Pangallo, Alan Perotti +2
Agent-Based Models (ABMs) are powerful tools for studying emergent properties in complex systems. In ABMs, agent behaviors are governed by local interactions and stochastic rules.…
Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction
Claudio Moroni, Claudio Borile, Carolina Mattsson +2
Link Prediction is a foundational task in Graph Representation Learning, supporting applications like link recommendation, knowledge graph completion and graph generation. Graph Ne…
Disentangled and Self-Explainable Node Representation Learning
Simone Piaggesi, André Panisson, Megha Khosla
Node representations, or embeddings, are low-dimensional vectors that capture node properties, typically learned through unsupervised structural similarity objectives or supervised…
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image Classifiers
Francesco Dibitonto, Fabio Garcea, André Panisson +2
Convolutional Neural Networks (CNNs) are nowadays the model of choice in Computer Vision, thanks to their ability to automatize the feature extraction process in visual tasks. Howe…
DINE: Dimensional Interpretability of Node Embeddings
Simone Piaggesi, Megha Khosla, André Panisson +1
Graphs are ubiquitous due to their flexibility in representing social and technological systems as networks of interacting elements. Graph representation learning methods, such as…
Evaluating Link Prediction Explanations for Graph Neural Networks
Claudio Borile, Alan Perotti, André Panisson
Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains. Providing human-understandable explanations fo…
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers
Alan Perotti, Simone Bertolotto, Eliana Pastor +1
Images are loaded with semantic information that pertains to real-world ontologies: dog breeds share mammalian similarities, food pictures are often depicted in domestic environmen…
Fast and Effective GNN Training through Sequences of Random Path Graphs
Francesco Bonchi, Claudio Gentile, Francesco Paolo Nerini +2
We present GERN, a novel scalable framework for training GNNs in node classification tasks, based on effective resistance, a standard tool in spectral graph theory. Our method prog…
The Thin Ideology of Populist Advertising on Facebook during the 2019 EU Elections
Arthur Capozzi, Gianmarco De Francisci Morales, Yelena Mejova +2
Social media has been an important tool in the expansion of the populist message, and it is thought to have contributed to the electoral success of populist parties in the past dec…
Adversarial Online Collaborative Filtering
Stephen Pasteris, Fabio Vitale, Mark Herbster +2
We investigate the problem of online collaborative filtering under no-repetition constraints, whereby users need to be served content in an online fashion and a given user cannot b…
Global misinformation spillovers in the online vaccination debate before and during COVID-19
Jacopo Lenti, Kyriaki Kalimeri, André Panisson +4
Anti-vaccination views pervade online social media, fueling distrust in scientific expertise and increasing vaccine-hesitant individuals. While previous studies focused on specific…
Echoes through Time: Evolution of the Italian COVID-19 Vaccination Debate
Giuseppe Crupi, Yelena Mejova, Michele Tizzani +2
Twitter is one of the most popular social media platforms in the country, but pre-pandemic vaccination debate has been shown to be polarized and siloed into echo chambers. It is th…
Revealing the determinants of gender inequality in urban cycling with large-scale data
Alice Battiston, Ludovico Napoli, Paolo Bajardi +4
Cycling is an outdoor activity with massive health benefits, and an effective solution towards sustainable urban transport. Despite these benefits and the recent rising popularity…
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs
Alan Perotti, Paolo Bajardi, Francesco Bonchi +1
Most methods for explaining black-box classifiers (e.g. on tabular data, images, or time series) rely on measuring the impact that removing/perturbing features has on the model out…
Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data
Serena Giurgola, Simone Piaggesi, Márton Karsai +3
Ending poverty in all its forms everywhere is the number one Sustainable Development Goal of the UN 2030 Agenda. To monitor the progress towards such an ambitious target, reliable,…
Developing Annotated Resources for Internal Displacement Monitoring
Fabio Poletto, Yunbai Zhang, Andre Panisson +3
This paper describes in details the design and development of a novel annotation framework and of annotated resources for Internal Displacement, as the outcome of a collaboration w…
Clandestino or Rifugiato? Anti-immigration Facebook Ad Targeting in Italy
Arthur Capozzi, Gianmarco De Francisci Morales, Yelena Mejova +3
Monitoring advertising around controversial issues is an important step in ensuring accountability and transparency of political processes. To that end, we use the Facebook Ads Lib…
FairLens: Auditing Black-box Clinical Decision Support Systems
Cecilia Panigutti, Alan Perotti, Andrè Panisson +2
The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare. The dete…
Facebook Ads: Politics of Migration in Italy
Arthur Capozzi, Gianmarco De Francisci Morales, Yelena Mejova +3
Targeted online advertising is on the forefront of political communication, allowing hyper-local advertising campaigns around elections and issues. In this study, we employ a new r…
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling
Simone Piaggesi, André Panisson
Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms. S…