12 citations · 12 across the 4 of their papers we have counts for
4 papers
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
Davide Buffelli, Pietro Liò, Fabio Vandin
In the past few years, graph neural networks (GNNs) have become the de facto model of choice for graph classification. While, from the theoretical viewpoint, most GNNs can operate…
Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach
Davide Buffelli, Fabio Vandin
Graph Neural Networks (GNNs) have become the state-of-the-art method for many applications on graph structured data. GNNs are a model for graph representation learning, which aims…
An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets
Adam Kirsch, Michael Mitzenmacher, Andrea Pietracaprina +3
As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is be…
MADMX: A Novel Strategy for Maximal Dense Motif Extraction
Roberto Grossi, Andrea Pietracaprina, Nadia Pisanti +3
We develop, analyze and experiment with a new tool, called MADMX, which extracts frequent motifs, possibly including don't care characters, from biological sequences. We introduce…