70 citations · 118 across the 10 of their papers we have counts for
10 papers
LGDC: Latent Graph Diffusion via Spectrum-Preserving Coarsening
Nagham Osman, Keyue Jiang, Davide Buffelli +2
Graph generation is a critical task across scientific domains. Existing methods fall broadly into two categories: autoregressive models, which iteratively expand graphs, and one-sh…
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs
Davide Buffelli, Farzin Soleymani, Bastian Rieck
Graph neural networks have become the default choice by practitioners for graph learning tasks such as graph classification and node classification. Nevertheless, popular graph neu…
Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?
Davide Buffelli, Ashish Gupta, Agnieszka Strzalka +1
Recommender systems have become fundamental building blocks of modern online products and services, and have a substantial impact on user experience. In the past few years, deep le…
Extending Logic Explained Networks to Text Classification
Rishabh Jain, Gabriele Ciravegna, Pietro Barbiero +3
Recently, Logic Explained Networks (LENs) have been proposed as explainable-by-design neural models providing logic explanations for their predictions. However, these models have o…
Scalable Regularization of Scene Graph Generation Models using Symbolic Theories
Davide Buffelli, Efthymia Tsamoura
Several techniques have recently aimed to improve the performance of deep learning models for Scene Graph Generation (SGG) by incorporating background knowledge. State-of-the-art t…
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…
A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
Davide Buffelli, Fabio Vandin
Graph Neural Networks (GNNs) are a framework for graph representation learning, where a model learns to generate low dimensional node embeddings that encapsulate structural and fea…
Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation
Davide Buffelli, Fabio Vandin
Sensor-based human activity recognition (HAR) requires to predict the action of a person based on sensor-generated time series data. HAR has attracted major interest in the past fe…
The Impact of Global Structural Information in Graph Neural Networks Applications
Davide Buffelli, Fabio Vandin
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours.…