Pasquale De Meo

University of Messina | UNIME

Sentiment analysis has become an indispensable tool across various domains, including political communication, marketing analytics, and finance. In the financial sector, sentiments such as confidence or fear play a pivotal role in shaping market dynamics, influencing supply and demand, and precipitating significant price fluctuations. The timely ex...

Network disruption is pivotal in understanding the robustness and vulnerability of complex networks, which is instrumental in devising strategies for infrastructure protection, epidemic control, cybersecurity, and combating crime. In this paper, with a particular focus on disrupting criminal networks, we proposed to impose a within-the-largest-conn...

Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used t...

Despite the huge importance that the centrality metrics have in understanding the topology of a network, too little is known about the effects that small alterations in the topology of the input graph induce in the norm of the vector that stores the node centralities. If so, then it could be possible to avoid re-calculating the vector of centrality...

In recent years, Knowledge Graphs (KGs) have played a crucial role in the development of advanced knowledge-intensive applications, such as recommender systems and semantic search. However, the human sensory system is inherently multi-modal, as objects around us are often represented by a combination of multiple signals, such as visual and textual....

Most existing cross-domain recommendation (CDR) systems apply the embedding and mapping idea to tackle the cold-start user problem and, to this end, they learn a common bridge function to transfer the user preferences from the source domain into the target domain. However, sharing a bridge function for all users inevitably leads to biased recommend...

Traditional community detection models either ignore the feature space information and require a large amount of domain knowledge to define the meta-paths manually, or fail to distinguish the importance of different meta-paths. To overcome these limitations, we propose a novel heterogeneous graph community detection method (called KGNN_HCD, heterog...

The problem of detecting communities in real-world networks has been extensively studied in the past, but most of the existing approaches work on single-domain networks, i.e. they consider only one type of relationship between nodes. Single-domain networks may contain noisy edges and they may lack some important information. Thus, some authors have...

Hypergraph neural networks have gained substantial popularity in capturing complex correlations between data items in multimodal datasets. In this study, we propose a novel approach called the self-supervised hypergraph learning (SHL) framework that focuses on extracting hypergraph features to improve multimodal representation. Our method utilizes...

Text classification is an emerging topic in the field of text data mining, but the current methods of deducing sentence polarity have two major shortcomings: on the one hand, there is currently a lack of a large and well-curated corpus; on the other hand, current solutions based on deep learning are particularly vulnerable to attacks from adversari...

The problem of detecting key nodes in a network (i.e. nodes with the greatest ability to spread an infection) has been studied extensively in the past. Some approaches to key node detection compute node centrality, but there is no formal proof that central nodes also have the greatest spreading capacity. Other methods use epidemiological models (e....

Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation, such as knowledge graphs. Since all nodes are connected directly, node global representation encoding enables direct communication between two distant nodes while disregarding graph topology. Node local representation encoding...

Graph robustness upon node failures state-of-art is huge. However, not enough is known on the effects of centrality metrics ranking after graph perturbations. To fill this gap, our aim is to quantify how much small graph perturbations will affect the centrality metrics. Thus, we considered two type of probabilistic failure models (i.e., Uniform and...

Random walks simulate the randomness of objects, and are key instruments in various fields such as computer science, biology and physics. The counter part of classical random walks in quantum mechanics are the quantum walks. Quantum walk algorithms provide an exponential speedup over classical algorithms. Classical and quantum random walks can be a...

With the development of deep learning and other technologies, the research of information propagation prediction has also achieved important research achievements. However, the existing information diffusion studies either focus on the attention relationships of users or they predict the information according to the diffusion relationships of users...

Social Network Analysis (SNA) is an interdisciplinary science that focuses on discovering the patterns of individuals interactions. In particular, practitioners have used SNA to describe and analyze criminal networks to highlight subgroups, key actors, strengths and weaknesses in order to generate disruption interventions and crime prevention syste...

Traditional methods for influential node identification usually require time consuming network traversal to select the candidate node set. In this article we propose a new influence nodes identification method, called Community-based Backward Generating Network (CBGN). First, the influence maximization framework is built by integrating community de...

Previous key node identification approaches assume that the transmission of information on a path always ends positively, which is not necessarily true. In this paper, we propose a new centrality index called Information Rank (IR for short) that associates each path with a score specifying the probability that such path successfully conveys a messa...

Social Network Analysis (SNA) is an interdisciplinary science that focuses on discovering the patterns of individuals interactions. In particular, practitioners have used SNA to describe and analyze criminal networks to highlight subgroups, key actors, strengths and weaknesses in order to generate disruption interventions and crime prevention syste...

Random walks simulate the randomness of objects, and are key instruments in various fields such as computer science, biology and physics. The counter part of classical random walks in quantum mechanics are the quantum walks. Quantum walk algorithms provide an exponential speedup over classical algorithms. Classical and quantum random walks can be a...

Graph Neural Networks (GNNs, in short) are a powerful computational tool to jointly learn graph structure and node/edge features. They achieved an unprecedented accuracy in the link prediction problem, namely the task of predicting if two nodes are likely to be tied by an edge in the near future. However, GNNs capture node attributes as scalars and...

Entity alignment is critical for multiple knowledge graphs (KGs) integration. Although researchers have made significant efforts to explore the relational embeddings between different KGs, existing approaches may not describe multi-modal knowledge well in some tasks, e.g., entity alignment. In this paper, we propose DFMKE, a dual fusion multi-modal...

Node-removal processes are generally used to test network robustness against failures, to verify the strength of a power grid, or to contain fake news. Yet, a node-removal task is typically assumed to be always successful; we argue that this is unrealistic, and that the node strengths should also be considered to better accommodate network failure...

Aiming at the problem of over-sampling for high-degree nodes and low-degree nodes in current sampling algorithms, a node Neighborhood Clustering coefficient Hierarchical Random Walk (NCHRW) sampling method is proposed. Firstly, the idea of hierarchy and degree distribution are adopted, and the k-means clustering algorithm is used to determine the v...

Target-specific sentiment analysis is an emerging topic in the field of text mining but current approaches to deriving the polarity of a sentence suffer from two main drawbacks: on one hand, we lack of a large and well-curated corpus, and on the other hand, current solutions based on deep learning are particularly vulnerable to the attack of advers...

Traditional social network analysis can be generalized to model some networked systems by multilayer structures where the individual nodes develop relationships in multiple layers. A multilayer network is called multiplex if each layer shares at least one node with some other layer. In this paper, we built a unique criminal multiplex network from t...

As most of the community discovery methods are researched by static thought, some community discovery algorithms cannot represent the whole dynamic network change process efficiently. This paper proposes a novel dynamic community discovery method (Phylogenetic Planted Partition Model, PPPM) for phylogenetic evolution. Firstly, the time dimension is...

Training Artificial Neural Networks (ANNs) is a non-trivial task. In the last years, there has been a growing interest in the academic community in understanding how those structures work and what strategies can be adopted to improve the efficiency of the trained models. Thus, the novel approach proposed in this paper is the inclusion of the entrop...

In view that the K-shell decomposition method can only effectively identify a single most influential node, but cannot accurately identify a group of most influential nodes, this article proposes a hybrid method based on K-shell decomposition to identify the most influential spreaders in complex networks. First, the K-shell decomposition method is...

Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases mul...

Real-world complex systems can be modeled as homogeneous or heterogeneous graphs composed by nodes connected by edges. The importance of nodes and edges is formally described by a set of measures called centralities which are typically studied for graphs of small size. The proliferation of digital collection of data has led to huge graphs with bill...

Recently, Social Network Analysis studies have led to an improvement and to a generalization of existing tools to networks with multiple subsystems and layers of connectivity. These kind of networks are usually called multilayer networks. Multilayer networks in which each layer shares at least one node with some other layer in the network are calle...

Recently, Social Network Analysis studies have led to an improvement and to a generalization of existing tools to networks with multiple subsystems and layers of connectivity. These kind of networks are usually called multilayer networks. Multilayer networks in which each layer shares at least one node with some other layer in the network are calle...

The importance of a node in a social network is identified through a set of measures called centrality. Degree centrality, closeness centrality, betweenness centrality and clustering coefficient are the most frequently used metrics to compute node centrality. Their computational complexity in some cases makes unfeasible, when not practically imposs...

Social Network Analysis is the use of Network and Graph Theory to study social phenomena, which was found to be highly relevant in areas like Criminology. This chapter provides an overview of key methods and tools that may be used for the analysis of criminal networks, which are presented in a real-world case study. Starting from available juridica...

Data collected in criminal investigations may suffer from: (i) incompleteness, due to the covert nature of criminal organisations; (ii) incorrectness, caused by either unintentional data collection errors and intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times...

Social Network Analysis (SNA) is used to study the exchange of resources among individuals, groups, or organizations. The role of individuals or connections in a network is described by a set of centrality metrics which represent one of the most important results of SNA. Degree, closeness, betweenness and clustering coefficient are the most used ce...

Social Network Analysis is the use of Network and Graph Theory to study social phenomena, which was found to be highly relevant in areas like Criminology. This chapter provides an overview of key methods and tools that may be used for the analysis of criminal networks, which are presented in a real-world case study. Starting from available juridica...

Network Science is an active research field, with numerous applications in areas like computer science, economics, or sociology. Criminal networks, in particular, possess specific topologies which allow them to exhibit strong resilience to disruption. Starting from a dataset related to meetings between members of a Mafia organization which operated...

Social Network Analysis (SNA) is used to study the exchange of resources among individuals, groups, or organizations. The role of individuals or connections in a network is described by a set of centrality metrics which represent one of the most important results of SNA. Degree, closeness, betweenness and clustering coefficient are the most used ce...

The development of deep learning has led to a dramatic increase in the number of applications of artificial intelligence. However, the training of deeper neural networks for stable and accurate models translates into artificial neural networks (ANNs) that become unmanageable as the number of features increases. This work extends our earlier study w...

Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to Law-Enforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs,...

Centrality metrics have been widely applied to identify the nodes in a graph whose removal is effective in decomposing the graph into smaller sub-components. The node--removal process is generally used to test network robustness against failures. Most of the available studies assume that the node removal task is always successful. Yet, we argue tha...

When it comes to collaboration within huge agents’ networks, trust management becomes a pivotal issue. Defying tool for a fast and efficient partner selection, even in lack of direct information, is of paramount importance, as much as possessing mechanisms allowing a matching between a selected task and a reliable agent able to carry it out. Direct...

Link prediction exercises may prove particularly challenging with noisy and incomplete networks, such as criminal networks. Also, the link prediction effectiveness may vary across different relations within a social group. We address these issues by assessing the performance of different link prediction algorithms on a mafia organization. The analy...

Navigability is a distinctive features of graphs associated with artificial or natural systems whose primary goal is the transportation of information or goods. We say that a graph $\mathcal{G}$ is navigable when an agent is able to efficiently reach any target node in $\mathcal{G}$ by means of local routing decisions. In a social network navigabil...

We consider information diffusion on Web-like networks and how random walks can simulate it. A well-studied problem in this domain is Partial Cover Time, i.e., the calculation of the expected number of steps a random walker needs to visit a given fraction of the nodes of the network. We notice that some of the fastest solutions in fact require that...

Compared to other types of social networks, criminal networks present hard challenges, due to their strong resilience to disruption, which poses severe hurdles to law-enforcement agencies. Herein, we borrow methods and tools from Social Network Analysis to (i) unveil the structure of Sicilian Mafia gangs, based on two real-world datasets, and (ii)...

Deep Learning opened artificial intelligence to an unprecedented number of new applications. A critical success factor is the ability to train deeper neural networks, striving for stable and accurate models. This translates into Artificial Neural Networks (ANN) that become unmanageable as the number of features increases. The novelty of our approac...

In this paper, we focus on the study of Sicilian Mafia organizations through Social Network Analysis. We analyse datasets reflecting two different Mafia Families, based on examinations of digital trails and judicial documents, respectively. The first dataset includes the phone calls logs among suspected individuals. The second one is based on polic...

Navigability is a distinctive features of graphs associated with artificial or natural systems whose primary goal is the transportation of information or goods. We say that a graph is navigable when an agent is able to efficiently reach any target node in by means of local routing decisions. In a social network navigability translates to the abilit...

In this article, we propose the PTP-MF (Pairwise Trust Prediction through Matrix Factorisation) algorithm, an approach to predicting the intensity of trust and distrust relations in Online Social Networks (OSNs). Our algorithm maps each OSN user i onto two low-dimensional vectors, namely, the trustor profile (describing her/his inclination to trust...

Background and objective: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients. Methods: To achieve our aim, machine learning techniques have been used. Two datasets have been taken into consideration: the...

It is our great pleasure to present to you the second edition of this special issue discussing the analysis and applications of complex social networks. Similarly to the one published in this journal last year, this one also turned out to be a great success as we managed to attract a number of high-quality researches in the area of complex social n...

The groundbreaking experiment of Travers and Milgram demonstrated the so-called "six degrees of separation" phenomenon, by which any individual in the world is able to contact an arbitrary, hitherto-unknown, individual by means of a short chain of social ties. Despite the large number of empirical and theoretical studies to explain the Travers-Milg...

An important issue in Online Social Networks consists of the capability to generate useful recommendations for users, as peers to contact in order to establish friendships and collaborations, interesting resources to use and so on. This implies the necessity of evaluating the trustworthiness a user should assign to other members of his/her online c...

Social networks are everywhere and research aiming at analysing and understanding these structures is growing year by year as its outcomes enable us to understand different social phenomena including social structures evolution, communities, spread over networks, and dynamics of changes in networks. This huge interest in the analysis of large-scale...

Graph robustness--the ability of a graph to preserve its connectivity after the loss of nodes and edges--has been extensively studied to quantify how social, biological, physical, and technical systems withstand to external damages. In this paper, we prove that graph robustness can be quickly estimated through the Randic index, a parameter introduc...

Online Social Networks are suitable environments for e-Learning for several reasons. First of all, there are many similarities between social network groups and classrooms. Furthermore, trust relationships taking place within groups can be exploited to give to the users the needed motivations to be engaged in classroom activities. In this paper we...

In this work we investigate on the time-stability of the homogeneity — in terms of mutual users’ similarity within groups — into real Online Social Networks by taking into account users’ behavioural information as personal interests. To this purpose, we introduce a conceptual framework to represents the time evolution of the group formation in an O...

E-Learning class formation will take benefit if common learners’ needs are taken into account. For instance, the availability of trust relationships among users can represent an additional motivation for classmates to engage activities. Common experience also suggests that there are many similarities within dynamics of formation for thematic social...

In collaborativeWeb-based platforms, user reputation scores are generally computed according to two orthogonal perspectives: (a) helpfulness-based reputation (HBR) scores and (b) centrality-based reputation (CBR) scores. InHBR approaches, the most reputable users are those who post the most helpful reviews according to the opinion of the members of...

Detecting communities in graphs is a fundamental tool to understand the structure of Web-based systems and predict their evolution. Many community detection algorithms are designed to process undirected graphs (i.e., graphs with bidirectional edges) but many graphs on the Web - e.g. microblogging Web sites, trust networks or the Web graph itself -...

Resilience identifies the ability of criminal networks to face pressures from law enforcement agencies and rapidly reorganize after perturbations or destabilizing attacks. Apart from environmental considerations, this concept is strongly tied to the topology of criminal networks which, unlike social networks, can be configured as hierarchical, cell...

Through online social network analysis, the emergence (over time) of “trusted” users is investigated, by studying the evolution of topological and centrality measures of the network of trust within the overall social network. Large datasets of user activity are studied from Ciao and Epinions (two online platforms with an explicit notion of trust co...

Social Sciences identify similarity and mutual trust as main criteria to consider in group formation processes. On this basis, we present a group formation technique which exploits measures of both similarity and trust, in order to improve the compactness of groups in Online Social Networks. Similarity and trust have been jointly exploited to desig...

In this paper we present the results of the study of Sicilian Mafia organization by using Social Network Analysis. The study investigates the network structure of a Mafia organization, describing its evolution and highlighting its plasticity to interventions targeting membership and its resilience to disruption caused by police operations. We analy...

In this paper, a distributed approach aimed at improving the quality of service in dynamic grid federations is presented. Virtual organizations (VO) are grouped into large-scale federations in which the original goals and scheduling mechanisms are left unchanged, while grid nodes can be quickly instructed to join or leave any VO at any time. Moreov...