Giuseppe Mangioni

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.

Identifying the community structure of the international-trade multi-network

We study the community structure of the multi-network of commodity-specific trade relations among world countries over the 1992–2003 period. We compare structures across commodities and time by means of the normalized mutual information index (NMI). We also compare them with exogenous community structures induced by geography and regional trade agreements. We find that commodity-specific community structures are very heterogeneous and much more fragmented than that characterizing the aggregate ITN. This shows that the aggregate properties of the ITN may result (and be very different) from the aggregation of very diverse commodity-specific layers of the multi-network. We also show that commodity-specific community structures, especially those related to the chemical sector, are becoming more and more similar to the aggregate one. Finally, our findings suggest that geography-induced partitions of our set of countries are much more correlated with observed community structures than partitions induced by regional-trade agreements. This result strengthens previous findings from the empirical literature on trade.

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.

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.

Users' attachment in trust networks: reputation vs. effort

Trust and reputation systems gained great attention in the last decades. Such systems aim at giving users a quantitative evaluation about the trustworthiness of other users. Several metrics achieve it by combining the past direct experience of all the users of the trust network. Among the methods developed to assess trust, EigenTrust is one of the most popular, based on an adaption of the PageRank metric to trust systems. In this paper, we investigate EigenTrust dynamics, trying to explain some hidden behavioural patterns. Our aim is to show that EigenTrust metric exhibits some counterintuitive behaviours when compared to the common sense notion of trust.

Community Structure in the Multi-network of International Trade

We study the community structure of the multi-network of commodity-specific and aggregate trade relations among world countries over the 1992-2003 period. We compare structures across products and time by means of the normalized mutual information index (NMI). We also compare them with exogenous community structures induced by geographical distances and regional trade agreements. We find that: (i) plastics and mineral fuels —and in general commodities belonging to the chemical sector— have the highest similarity with aggregate trade communities; (ii) both at aggregated and disaggregated levels, physical variables such as geographical distance are more correlated with the observed trade fluxes than regional-trade agreements.