Online social platforms have become a fertile ground for the rapid spread of extremist narratives, yet traditional single-layer network analyses overlook the interplay of content, timing, and polarization that fuels radicalization. We introduce the Integrated Multilayer Reinforcement Model (IMRM), a unified framework that represents each user as a node in four interdependent layers (interaction, content similarity, temporal dynamics, and sentiment) and explicitly encodes feedback loops via uniform interlayer coupling. We define three novel measures: Composite Reinforced Centrality (CRC), which multiplicatively aggregates a user’s normalized influence across layers; Temporal Burst Influence (TBI), which captures episodic surges in activity; and Sentiment Synchronization Coefficient (SSC), which quantifies emotional alignment with peers. We derive four theoretical propositions linking these measures to radicalization processes and validate them on Reddit data surrounding five major United States socio-political events. Our experiments reveal that (i) high CRC users have a high probability of being radicalized, (ii) radicalized users have higher values of both TBI and SSC, (iii) bridging nodes linking different communities exhibit elevated CRC, and (iv) CRC remains a robust, context-invariant predictor of radical engagement. The findings aim at highlighting how online radicalization emerges from the synergistic fusion of who you interact with, what you share, when you act, and how you feel.

Abstract In recent years, Transformers have revolutionized the management of Natural Language Processing tasks, and Vision Transformers (ViTs) promise to do the same for Computer Vision ones. However, the adoption of ViTs is hampered by their computational cost. Indeed, given an image divided into patches, it is necessary to compute for each layer the attention of each patch with respect to all the others. Researchers have proposed many solutions to reduce the computational cost of attention layers by adopting techniques such as quantization, knowledge distillation and manipulation of input images. In this paper, we aim to contribute to the solution of this problem. In particular, we propose a new framework, called AgentViT, which uses Reinforcement Learning to train an agent that selects the most important patches to improve the learning of a ViT. The goal of AgentViT is to reduce the number of patches processed by a ViT, and thus its computational load, while still maintaining competitive performance. We tested AgentViT on CIFAR10, FashionMNIST, and Imagenette $$^+$$ + (which is a subset of ImageNet) in the image classification task and obtained promising performance when compared to baseline ViTs and other related approaches available in the literature.

In recent years, the enormous development of Machine Learning, especially Deep Learning, has led to the widespread adoption of Artificial Intelligence (AI) systems in a large variety of contexts. Many of these systems provide excellent results but act as black-boxes. This can be accepted in various contexts, but there are others (e.g., medical ones) where a result returned by a system cannot be accepted without an explanation on how it was obtained. Explainable AI (XAI) is an area of AI well suited to explain the behavior of AI systems that act as black-boxes. In this paper, we propose a model-agnostic XAI framework to explain the behavior of classifiers. Our framework is based on network theory; thus, it is able to make use of the enormous amount of results that researchers in this area have discovered over time. Being network-based, our framework is completely different from the other model-agnostic XAI approaches. Furthermore, it is parameter-free and is able to handle heterogeneous features that may not even be independent of each other. Finally, it introduces the notion of dyscrasia that allows us to detect not only which features are important in a particular task but also how they interact with each other.

Abstract In this paper, we propose a framework that uses the theory and techniques of (Social) Network Analysis to investigate the learned representations of a Graph Neural Network (GNN, for short). Our framework receives a graph as input and passes it to the GNN to be investigated, which returns suitable node embeddings. These are used to derive insights on the behavior of the GNN through the application of (Social) Network Analysis theory and techniques. The insights thus obtained are employed to define a new training loss function, which takes into account the differences between the graph received as input by the GNN and the one reconstructed from the node embeddings returned by it. This measure is finally used to improve the performance of the GNN. In addition to describe the framework in detail and compare it with related literature, we present an extensive experimental campaign that we conducted to validate the quality of the results obtained.

Abstract Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High-Utility Pattern Mining; in particular, we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach.

Social platforms are the preferred medium for many people to express their opinions on many topics. This has led many professionals from various fields (marketing, politics, research and development, etc.) to demand increasingly advanced approaches capable of analyzing the evolution of user or community sentiments on particular topics. In this paper, we want to make a contribution to addressing this issue. Specifically, we propose a model and a framework to analyze the dynamics of user and community sentiments in a social platform. In particular, our framework currently focuses on three activities, namely: (i) finding users capable of creating and maintaining a community that reflects their sentiment on a topic; (ii) studying how a user or community sentiment on a topic evolves over time; and (iii) investigating the cross-contamination between a user community and its neighborhood. We tested our framework by means of an extensive experimental campaign that we describe in the paper. Our framework is extremely scalable, and further activities can be easily implemented in it in the near future.

Deductive formalisms have been strongly developed in recent years; among them, Answer Set Programming (ASP) gained some momentum, and has been lately fruitfully employed in many real-world scenarios. Nonetheless, in spite of a large number of success stories in relevant application areas, and even in industrial contexts, deductive reasoning cannot be considered the ultimate, comprehensive solution to AI; indeed, in several contexts, other approaches result to be more useful. Typical Bioinformatics tasks, for instance classification, are currently carried out mostly by Machine Learning (ML) based solutions. In this paper, we focus on the relatively new problem of analyzing the evolution of neurological disorders. In this context, ML approaches already demonstrated to be a viable solution for classification tasks; here, we show how ASP can play a relevant role in the brain evolution simulation task. In particular, we propose a general and extensible framework to support physicians and researchers at understanding the complex mechanisms underlying neurological disorders. The framework relies on a combined use of ML and ASP, and is general enough to be applied in several other application scenarios, which are outlined in the paper.

In this paper, we propose a method for the computation of a novel distance metrics, called Multi-Parameterized Edit Distance (MPED) among strings defined over heterogeneous alphabets. We show that the computation of MPED is hard and that several interesting application contexts can benefit from its application. We then present a novel imple- mentation strategy based on an Evolutionary Heuristics, which we experimentally demonstrate to be efficient and effective for the problem at hand. Our approach paves indeed the way to the adoption of this new metric in all those contexts in which involved strings come from heterogeneous sources, each adopting its own alphabet.

White Matter fibers play an important role in the working of brain. In order to improve their analysis, it is important to cluster them in homogeneous bundles. In this activity, the amount of data to process is huge, and an automated approach to carrying out this task is in order. Since fiber clustering should consider the position of fibers in the three-dimensional space, we are in presence of a multi-dimensional clustering problem. In this paper, we propose an automated approach to solving it. Our approach is based on a particular string representation of fibers and on a new string dissimilarity metric. Thanks to these two novelties, we can reduce the complex problem of White Matter fiber clustering to a much simpler and well-known string clustering problem. Interestingly, this way of proceeding can be extended to define other multi-view data applications, as well as to integrate (possibly heterogeneous) data coming from different domains.