Pasquale De Meo

Pasquale De Meo
Università degli Studi di Messina | UNIME

PhD

About

150
Publications
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3,242
Citations

Publications

Publications (150)
Article
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...
Article
Full-text available
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...
Article
Full-text available
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...
Chapter
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...
Article
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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...
Chapter
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...
Chapter
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...
Preprint
Full-text available
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...
Preprint
Full-text available
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...
Preprint
Full-text available
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...
Preprint
Full-text available
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...
Chapter
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...
Chapter
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...
Chapter
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...
Preprint
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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,...
Preprint
Full-text available
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...
Chapter
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...
Article
Full-text available
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...
Preprint
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...
Preprint
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...
Preprint
Full-text available
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)...
Chapter
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...
Chapter
Full-text available
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...
Conference Paper
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...
Article
Full-text available
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...
Article
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...
Article
Full-text available
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...
Preprint
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
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...
Conference Paper
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...
Article
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...
Article
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...
Article
Full-text available
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...
Article
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 -...
Article
Full-text available
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...
Article
Full-text available
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...
Conference Paper
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...
Article
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...
Article
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...
Conference Paper
Crowd-sourcing has become a popular form of computer mediated collaborative work and OpenStreetMap represents one of the most successful crowd-sourcing systems, where the goal of building and maintaining an accurate global map of the world is being accomplished by means of contributions made by over 1.2M citizens. However, within this apparently la...
Article
In Online Social Networks, in order to virally distribute some topics and, at the same time, protecting users from undesiredmessages, we propose to diffuse viral campaigns only on a second dimension of the social network. In the proposed approach, software agents assist the user by selecting the most appropriate campaigns for their owners. A users-...
Article
PERVASIVE SOCIO-TECHNICAL NETWORKS bring new conceptual and technological challenges to developers and users alike. A central research theme is evaluation of the intensity of relations linking users and how they facilitate communication and the spread of information. These aspects of human relationships have been studied extensively in the social s...
Conference Paper
A central research theme in the Online Social Network (OSN) scenario consists of predicting the trustworthiness a user should assign to the other OSN members. Past approaches to predict trust relied on global reputation models: they were based on feedbacks about the actions performed by the user in the past and provided for the entire OSN. These mo...
Conference Paper
Full-text available
The internal organization of an Online Social Network is well described by the formation of groups between members. Often groups evolve in a very confusing way, due to occasional interactions between their components. However, it does not necessarily imply the formation of aggregation units in which users have similar interests and behaviour and, c...
Article
Full-text available
Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, e...
Article
Full-text available
Understanding the dynamics behind group formation and evolution in social networks is considered an instrumental milestone to better describe how individuals gather and form communities, how they enjoy and share the platform contents, how they are driven by their preferences/tastes, and how their behaviors are influenced by peers. In this context,...
Article
Full-text available
In the fight against the racketeering and terrorism, knowledge about the structure and the organization of criminal networks is of fundamental importance for both the investigations and the development of efficient strategies to prevent and restrain crimes. Intelligence agencies exploit information obtained from the analysis of large amounts of het...
Article
Full-text available
The study of criminal networks using traces from heterogeneous communication media is acquiring increasing importance in nowadays society. The usage of communication media such as phone calls and online social networks leaves digital traces in the form of metadata that can be used for this type of analysis. The goal of this work is twofold: first w...
Article
Full-text available
Modern online social platforms enable their members to be involved in a broad range of activities like getting friends, joining groups, posting/commenting resources and so on. In this paper we investigate whether a correlation emerges across the different activities a user can take part in. To perform our analysis we focused on aNobii, a social pla...
Conference Paper
In this work we deal with the issue of improving the QoS provided by each node of a Grid Federation, by modelling it as a problem of “Grid formation”. In the proposed model each Grid node belonging to a computationalGrid, is free to join with or leave a grid with the goal of improving its satisfaction. Contextually, each grid is free to search othe...
Article
Full-text available
Social internetworking systems are becoming a challenging new reality; they group together multiple, and possibly heterogenous, social networks. The typical problems of social network research become much more complex in a social internetworking context. In this paper, we propose a conceptual framework, and an underlying model, to handle some of th...
Article
In this work we present an in-depth analysis of the user behaviors on different Social Sharing systems. We consider three popular platforms, Flickr, Delicious and StumbleUpon, and, by combining techniques from social network analysis with techniques from semantic analysis, we characterize the tagging behavior as well as the tendency to create frien...
Article
Full-text available
The problem of clustering large complex networks plays a key role in several scientific fields ranging from Biology to Sociology and Computer Science. Many approaches to clustering complex networks are based on the idea of maximizing a network modularity function. Some of these approaches can be classified as global because they exploit knowledge a...
Conference Paper
The formation and evolution of interest groups in Online Social Networks is driven by both the users' preferences and the choices of the groups' administrators. In this context, the notion of homogeneity of a social group is crucial: it accounts for determining the mutual similarity among the members of a group and it's often regarded as fundamenta...