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Graph-Informed Neural Networks
In this repository, we publish the codes necessary to implement the Graph-Informed Neural Networks (GINNs), presented for the first time in the paper: Graph-Informed Neural... -
Private Multi-Start Optimization Neural Networks
In this repository, we publish the codes necessary to implement the Multi-Start Optimization Neural Networks (MSO-NNs), presented fin the paper: Automatic... -
Private Alternate Training for Multi-Task Neural Networks
In this repository, we publish the code used to implement the Alternate Training through the Epochs (ATE) procedure for training Multi-Task Neural Networks (MTNN) presented in... -
Quantum Distance-Based Classifier
The Quantum Distance-Based Classifier is a technique inspired by the classical k-Nearest Neighbors that leverages quantum properties to perform prediction. -
Retweet cascade estimation and analysis
A collection of scripts for the estimation and analysis of a "retweet cascade", i.e., the tree structure describing how the original tweet was spread by retweeters. -
Twitter Monitor Library for Twitter API v2
A library to ease the creation and management of social media listening campaigns using the Twitter API v2. -
Network generator with fractal topologies - WP8
The micro-project has developed a python module for the generation of networks with fractal topologies, in particular Sierpinski graphs for the task involved in T8.4 Complex... -
GLocalX-C
A Python library to explain machine learning models by hierarchically aggregating single explanations of its predictions. Explanations are provided as decision rules... -
ArchiveSpark
ArchiveSpark is an Apache Spark framework for easy data access, processing, extraction as well as derivation for Web archives and archival collections. It has a simple and... -
Structify-Net
Structify_net is a python library allowing to create networks with a predefined structure, and a chosen number of nodes and links.-
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Attributed Sream Hypergraphs
ash_model is a Python software package that allows to represent and analyze dynamic hypergraphs enriched with node attributes.
