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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…
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…
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…
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…
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…