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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 friendship relationships of the users of these platforms. The aim of our investigation is to see if (and how) the features and goals of a given Social Sharing system reflect on the behavior of its users and, moreover, if there exists a correlation between the social and tagging behavior of the users. We report our findings in terms of the characteristics of user profiles according to three different dimensions: (i) intensity of user activities, (ii) tag-based characteristics of user profiles, and (iii) semantic characteristics of user profiles. [ABSTRACT FROM AUTHOR]

This paper presents a semi-automatic approach to deriving sub-schema similarities from semantically heterogeneous XML Schemas. The proposed approach is specific for XML, almost automatic and light. It consists of two phases: the first phase selects the most promising pairs of sub-schemas, the second one examines them and returns only those which are similar. This paper describes the approach in all details and illustrates a large variety of experiments to test its performance. Furthermore, it presents a comparison between this approach and others which have already been proposed in the literature. [ABSTRACT FROM AUTHOR]

The article examines the use of multiagent system for assisting citizens in searching e-government services. It explains that the adoption of intelligent agent technology helps satisfy the interests and needs of citizens. Detailed descriptions on the different components used in the system are provided. They include support databases, government agency databases and public administration agent. The accuracy achieved of the system using Citizen Profile Based (CPB) agent and Database Similarity and CPB (DS-CPB) together are evaluated.

This paper aims at studying the utilization of Intelligent Agents for supporting citizens to access e-government services. For this purpose, it proposes a multi-agent system capable of suggesting to the citizens the most interesting services for them; these suggestions are determined by considering both their needs/preferences and the capabilities of the devices used by them. The paper first describes the proposed system and, then, reports various experimental results. Finally, it presents a comparison between the proposed system and other related ones already presented in the literature. [ABSTRACT FROM AUTHOR]

This paper presents an approach for the semi-automatic, uniform extraction of synonymies, hyponymies, overlappings and homonymies holding among concepts of different XML Schemas. The proposed approach is specialized for XML, is almost automatic and "light". As a further, original, peculiarity, it is parametric w.r.t. a "severity level" against which the extraction task is performed. First the paper presents an overview of the interschema property extraction approaches already presented in the past, as well as a set of criteria for classifying this kind of approaches. After this, it describes the proposed approach in all details, illustrates various theoretical results, presents the experiments we have performed for testing it and compares it with the interschema property extraction approaches previously proposed in the literature. [ABSTRACT FROM AUTHOR]

In this paper we propose XICOMAS_Q, an XML-based multi-agent system for the Information Content oriented management of QoS in telecommunications networks. XICOMAS_Q is characterized by the following features: (i) it is strongly subjective, i.e. it handles network management activities according to user preferences represented by means of a user profile; (ii) it can operate on a large variety of telecommunications networks; (iii) it is semi-automatic; (iv) it is XML-based, i.e. it exploits XML for guaranteeing a light, versatile and standard mechanism for information management and exchange. The paper reports also various experimental results as well as a comparison between XICOMAS_Q and other related QoS management systems already presented in the literature. [ABSTRACT FROM AUTHOR]

A large amount of research is currently focusing on the issue of the adaptive control of the quality-of-service (QoS) provided to multimedia applications in heterogeneous wireless systems. In this paper, the authors aim at contributing to this issue by proposing a mechanism that exploits user profiling techniques and suitable QoS mapping functions to introduce the soft QoS idea into a wireless multimedia scenario. The research objective is a QoS control architecture, which enables the continuous convergence between the actual user preferences and expectations and the resource constraints of the underlying wireless system. The proposed architecture operates between the system and the application layer. This allows it to achieve the intended results, by means of an effective dynamic reconfiguration of the applications and the contemporary renegotiation of the wireless resources. [ABSTRACT FROM AUTHOR]

Covert networks refer to criminal organizations that operate outside the boundaries of the law; they can be mainly classified as terrorist networks and criminal networks. We consider how Social Network Analysis (SNA) is used to analyze such networks in order to attain a greater knowledge of criminal behavior. In fact, SNA allows examining the network structure and functioning by computing relevant metrics and parameters to identify roles, positions, features, and other network functioning that are not otherwise easily discovered at first glance. This is why Law Enforcement Agencies (LEAs) are showing growing interest in SNA, which is also used to identify weak spots and disrupt criminal groups. This paper provides a literature review and a classification of methods and real-case applications of disruption techniques. It considers covert network adaptability to such dismantling attempts, herein referred to as resilience. Critical problems of SNA in criminal studies are discussed, including data collection techniques and the inevitable incompleteness and biases of real-world datasets, with the aim of promoting a new research stream for both dismantling techniques and data collection issues. [ABSTRACT FROM AUTHOR]

• We describe a novel criminal dataset derived from an Italian crime case. • We extract two criminal networks capturing meetings/phone calls between suspected. • We tested many link prediction algorithms on our networks. • We investigated on the robustness of link prediction algorithms on criminal networks 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 analysis relies on an original dataset manually extracted from the judicial documents of operation "Montagna", conducted by the Italian law enforcement agencies against individuals affiliated with the Sicilian Mafia. To run our analysis, we extracted two networks: one including meetings and one recording telephone calls among suspects, respectively. We conducted two experiments on these networks. First, we applied several link prediction algorithms and observed that link prediction algorithms leveraging the full graph topology (such as the Katz score) provide very accurate results even on very sparse networks. Second, we carried out extensive simulations to investigate how the noisy and incomplete nature of criminal networks may affect the accuracy of link prediction algorithms. The experimental findings suggest the soundness of link predictions is relatively high provided that only a limited amount of knowledge about connections is hidden or missing, and the unobserved edges follow some kind of generative law. The different results on the meeting and telephone call networks indicate that the specific features of a network should be taken into careful consideration. [ABSTRACT FROM AUTHOR]

• A link prediction method called GCCL (Graph Conversion Capsule Link) is proposed. • A CapsNet layer is introduced in the edge feature map. • We carried out extensive experiments on six real networks. 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 such a representation can be non-optimal. In this paper we propose a link prediction approach which combines GNN with Capsule Networks (CapsNet), a powerful deep learning framework that obtained high quality representations when applied to image processing. Our approach first applies a GNN to generate node embeddings. Then, it makes use of a conversion block with the purpose of transforming node embeddings into a node pair feature map (called edge feature map). Because of such a transformation, the link prediction problem is equivalent to a graph classification problem. Finally, our approach applies CapsNets to learn the feature representation of the edge feature map, so that the attributes of the node pairs are captured from different aspects. We evaluate the feasibility and effectiveness of our approach (called Graph Conversion Capsule Link, GCCL in short) on six networks without node attributes and three networks with node attribute. Experimental results show that in both conditions (with and without node attributes) our approach is significantly more accurate than the competitor methods, with an average accuracy improvement of about 20%. [ABSTRACT FROM AUTHOR]

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-connected-component constraint in a continuous batch removal disruption process. Through a series of experiments on a recently released Sicilian Mafia network, we revealed that the constraint would enhance degree-based methods while weakening betweenness-based approaches. Moreover, based on the findings from the experiments using various disruption strategies, we propose a structurally-filtered greedy disruption strategy that integrates the effectiveness of greedy-like methods with the efficiency of structural-metric-based approaches. The proposed strategy significantly outperforms the longstanding state-of-the-art method of betweenness centrality while maintaining the same time complexity., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Jia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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 metrics if some minimal changes occur in the network topology, which would allow for significant computational savings. Hence, after formalising the notion of centrality, three of the most basic metrics were herein considered (i.e., Degree, Eigenvector, and Katz centrality). To perform the simulations, two probabilistic failure models were used to describe alterations in network topology: Uniform (i.e., all nodes can be independently deleted from the network with a fixed probability) and Best Connected (i.e., the probability a node is removed depends on its degree). Our analysis suggests that, in the case of degree, small variations in the topology of the input graph determine small variations in Degree centrality, independently of the topological features of the input graph; conversely, both Eigenvector and Katz centralities can be extremely sensitive to changes in the topology of the input graph. In other words, if the input graph has some specific features, even small changes in the topology of the input graph can have catastrophic effects on the Eigenvector or Katz centrality., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Cavallaro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)