Vincenza Carchiolo

Extending the definition of modularity to directed graphs with overlapping communities

Complex network topologies present interesting and surprising properties, such as community structures, which can be exploited to optimize communication, to find new efficient and context-aware routing algorithms or simply to understand the dynamics and meaning of relationships among nodes. Complex networks are gaining more and more importance as a reference model and are a powerful interpretation tool for many different kinds of natural, biological and social networks, where directed relationships and contextual belonging of nodes to many different communities is a matter of fact. This paper starts from the definition of a modularity function, given by Newman to evaluate the goodness of network community decompositions, and extends it to the more general case of directed graphs with overlapping community structures. Interesting properties of the proposed extension are discussed, a method for finding overlapping communities is proposed and results of its application to benchmark case-studies are reported. We also propose a new data set which could be used as a reference benchmark for overlapping community structures identification.

Reliable peers and useful resources: Searching for the best personalised learning path in a trust- and recommendation-aware environment

The evolution of the Web 2.0 and the intensive use of peer-to-peer networks allow us to access more and more information from disparate data sources than in the past, thus making life-long learning more effective. In this scenario, a critical issue still remains to be addressed: the reliability of resources, whether they can be recommended as useful and the reliability of peers, whether it is possible to trust them as providers. We propose to integrate these concepts with e-learning, proposing a model for searching for personalised and useful learning paths suggested by reliable (trusted) peers. We performed simulations on the Merlot data set enhanced with information extracted from Advogato, Epinions and Ariadne data sets, testing the efficiency and effectiveness of the proposed approach.

Hardware/software synthesis of formal specifications in codesign of embedded systems

CoDesign aims to integrate the design techniques of hardware and software. In this work, we present a CoDesign methodology based on a formal approach to embedded system specification. This methodology uses the Templated T-LOTOS language to specify the system during all design phases. Templated T-LOTOS is a formal language based on CCS and CSP models. Using Templated T-LOTOS, a system can be specified by observing the temporal ordering in which the events occur from the outside. In this paper we focus on the synthesis of system specified by Templated T-LOTOS. The proposed synthesis algorithm takes advantage of peculiarities of Templates T-LOTOS. Hardware modules are translated into a register transfer-level language that manages some signals in order to drive synchronization, while the software models are translated into C according to a finite state model whose operations are controlled by a scheduler. The synthesis of the Templated T-LOTOS specification is based on the direct translation of the language operators to ensure that the implemented system is the same as the specified one.

Emerging structures of P2P networks induced by social relationships

Networks seem to be the natural way chosen by nature to organise individuals, resources and interactions in an effective and robust structure. Studies about natural networks focused on the central role of emerging structures in distributed environments, and pointed out some properties such as small-world effect and communities which are of the most importance to guarantee a fast and efficient communication among nodes. In this paper we propose a model for P2P networks which mimics behaviours of peers in social and biological networks and naturally evolves to a robust graph of peers with some interesting properties, including small-world effect and community decomposition.

Courses personalization in an e-learning environment

The e-learning represents the new frontier of education, significantly improving the learning process. We propose an e-learning model, providing both teachers and students with an open and modular learning environment. We then focus on courses personalization, both in terms of contents and teaching materials, according to each students needs and capabilities, also taking teacher guidelines into account. To accomplish this, we model courses/lessons as graph nodes, where arcs represent their precedence/succession relationships. We outline a course generation/presentation engine which allows the creation of personalized learning paths (subgraph) by extracting lessons, eliminating those known to the student, and arranging them into a tree including all possible paths starting from the students possessed knowledge towards desired knowledge.

Trust assessment: a personalized, distributed, and secure approach

Currently several computer‐based scenarios leverage the concept of trust as a mean to make electronic interactions (e.g., e‐commerce transactions) as reliable as possible, allowing to cope with uncertainty and risks by recommending trusted peers. Generally, the evaluation of trustworthiness can be accomplished according to many principles, from social‐based to psychology‐based; one of the commonly adopted approaches within peer‐to‐peer networks, virtual social networks, and recommendation systems is the reputation‐based trust evaluation. Because more and more large networks (even with millions of nodes) aim at leveraging trust, approaches to its assessment have to take into account the factors as efficient distributed implementation and effective security protection against malicious attacks.

A model for a web-based learning system

The dissemination of knowledge is currently being improved by e-learning, which consists of a combination of teaching methodologies and computer-based tools. Recently e-learning environments have started to exploit web technology to provide a simple, flexible, distributed and open platform. In this paper we propose a model for an e-learning system, aiming at sharing both course contents and teaching materials, in order to provide students with a single and uniform set of concepts to be learned, and promoting active learning by allowing the construction of courses which are personalized in terms of both contents and teaching materials, selected according to each student’s needs and capabilities. A first, open-source prototype based on the proposed model has been implemented to validate the model.

Learning through ad-hoc formative paths

During the last decade, the interest in distance learning tools has grown thanks to the availability of bandwidth and powerful computers. Thus, distance learning has moved from a particular environments (such as industry), to a larger community. The main challenge of last generation e-learning tools is to provide courses tailored to the different student backgrounds; this pushes research to create an adaptive environment able to just-in-time craft the best path for each student. The paper deals with this problem, also presenting a Web based prototype of an e-learning tool to provide users with all paths, moving from knowledge of a student to desired knowledge.

Communities unfolding in multislice networks

Discovering communities in complex networks helps to understand the behaviour of the network. Some works in this promising research area exist, but communities uncovering in time-dependent and/or multiplex networks has not deeply investigated yet. In this paper, we propose a communities detection approach for multislice networks based on modularity optimization. We first present a method to reduce the network size that still preserves modularity. Then we introduce an algorithm that approximates modularity optimization (as usually adopted) for multislice networks, thus finding communities. The network size reduction allows us to maintain acceptable performances without affecting the effectiveness of the proposed approach.

An adaptive overlay network inspired by social behaviour

Nature is a great source of inspiration for scientists, because natural systems seem to be able to find the best way to solve a given problem by using simple and robust mechanisms. Studying complex natural systems, scientists usually find that simple local dynamics lead to sophisticated macroscopic structures and behaviour. It seems that some kind of local interaction rules naturally allow the system to auto-organize itself as an efficient and robust structure, which can easily solve different tasks. Examples of such complex systems are social networks, where a small set of basic interaction rules leads to a relatively robust and efficient communication structure. In this paper, we present PROSA, a semantic peer-to-peer (P2P) overlay network inspired by social dynamics. The way queries are forwarded and links among peers are established in PROSA resemble the way people ask other people for collaboration, help or information. Behaving as a social network of peers, PROSA naturally evolves to a small world, where all peers can be reached in a fast and efficient way. The underlying algorithm used for query forwarding, based only on local choices, is both reliable and effective: peers sharing similar resources are eventually connected with each other, allowing queries to be successfully answered in a really small amount of time. The resulting emergent structure can guarantee fast responses and good query recall.