Kolmogorov-Arnold Graph Neural Networks

Graph neural networks (GNNs) excel in learning from network-like data but often lack interpretability, making their application challenging in domains requiring transparent decision-making. We propose the Graph Kolmogorov-Arnold Network (GKAN), a novel GNN model leveraging spline-based activation functions on edges to enhance both accuracy and interpretability. Our experiments on five benchmark datasets demonstrate that GKAN outperforms state-of-the-art GNN models in node classification, link prediction, and graph classification tasks. In addition to the improved accuracy, GKAN's design inherently provides clear insights into the model's decision-making process, eliminating the need for post-hoc explainability techniques. This paper discusses the methodology, performance, and interpretability of GKAN, highlighting its potential for applications in domains where interpretability is crucial.

AI-based Data Preparation and Data Analytics in Healthcare: The Case of Diabetes

The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database. This paper presents the initial results of an ongoing project whose focus is the application of Artificial Intelligence and Machine Learning techniques for conceptualizing, cleaning, and analyzing such an important and valuable dataset, with the goal of providing predictive insights to better support diabetologists in their diagnostic and therapeutic choices.

Adaptive Positive-Unlabelled Learning via Markov Diffusion

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 been proposed to deal with PU learning. Some techniques consider the unlabelled samples as negative ones, reducing the problem to a binary classification with a noisy negative set, while others aim to detect sets of possible negative examples to later apply a supervised machine learning strategy (two-step techniques). The approach proposed in this work falls in the latter category and works in a semi-supervised fashion: motivated and inspired by previous works, a Markov diffusion process with restart is used to assign pseudo-labels to unlabelled instances. Afterward, a machine learning model, exploiting the newly assigned classes, is trained. The principal aim of the algorithm is to identify a set of instances which are likely to contain positive instances that were originally unlabelled.