Abstract: Many datasets are best represented as graphs of entities connected by relationships rather than as a single uniform dataset or table. Graph Neural Networks (GNNs) have been used to...
Neural Networks and Support Vector Machines
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Speaker: Federica Baccini
When: 10 Luglio 2023
Where: Aula Magna - DIAG
Title: Analysis of multiple relations in multilayer and higher-order networks
Abstract: This seminar focuses on the extension of...
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Title: Integrating Graph Representation Learning and Diffusion: Computational Models and Applications in Chemistry and Medicine.AbstractThe talk will focus on recent methodological novelties and challenges in graph...
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The RSTLess research group is a dynamic and innovative team of researchers from Sapienza University of Rome, led by Professor Fabrizio Silvestri.
Our focus is on the cutting-edge fields of Deep Learning, Information Retrieval, Graph Neural Networks, and Natural Language Processing, with a...
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Motivation: Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a disease due to a lack of studies is a challenging task in which prioritization based on prior knowledge is an...
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Graph neural networks (GNNs) recursively propagate signals along the edges of an input graph, integrate node feature information with graph structure, and learn object representations. Like other deep neural network models, GNNs have notorious black box character. For GNNs, only few approaches are...
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Here we present EdgeSHAPer, a workflow for explaining graph neural networks by approximating Shapley values using Monte Carlo sampling. In this protocol, we describe steps to execute Python scripts for a chemical dataset from the original publication; however, this approach is also applicable to...
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Abstract
Given the recent proliferation of disinformation online, there has been growing research interest in automatically debunking rumors, false claims, and "fake news". A number of fact-checking initiatives have been launched so far, both manual and automatic, but...
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Positive-Unlabelled (PU) learning is the machine learning setting in which only a set of positive instances are labelled, while the rest of the data set is unlabelled. The unlabelled instances may be either unspecified positive samples or true negative samples. Over the years, many solutions have...
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Convolutional Neural Networks (CNNs) have been widely used in the field of audio recognition and classification, since they often provide positive results. Motivated by the success of this kind of approach and the lack of practical methodologies for the monitoring of construction sites by using...