Antonino Nocera

Recommendation of similar users, resources and social networks in a Social Internetworking Scenario

In this paper we propose an approach to recommend to a user similar users, resources and social networks in a Social Internetworking Scenario. Our approach presents some interesting novelties with respect to the existing ones. First of all, it operates on a Social Internetworking context and not on a single social network. Moreover, it considers not only explicit relationships among users but also the implicit ones, connecting users on the basis of shared interests and behavior; the latter is derived from the analysis of user actions in the considered Social Internetworking Scenario. In addition, it considers the presence of possible semantic anomalies involving the description of available users, resources and social networks. Finally, it takes into account not only the local information regarding involved users, resources and social networks but also the global one, i.e., the information spread all over the considered Social Internetworking Scenario.

Discovering links among social networks

Distinct social networks are interconnected via bridge users, who play thus a key role when crossing information is investigated in the context of Social Internetworking analysis. Unfortunately, not always users make their role of bridge explicit by specifying the so-called me edge (i.e., the edge connecting the accounts of the same user in two distinct social networks), missing thus a potentially very useful information. As a consequence, discovering missing me edges is an important problem to face in this context yet not so far investigated. In this paper, we propose a common-neighbors approach to detecting missing me edges, which returns good results in real life settings. Indeed, an experimental campaign shows both that the state-of-the-art common-neighbors approaches cannot be effectively applied to our problem and, conversely, that our approach returns precise and complete results.

Moving from social networks to social internetworking scenarios: The crawling perspective

In new generation social networks, we expect that the paradigm of Social Internetworking Systems (SISs) will become progressively more important. Indeed, the possibility of interconnecting users and resources of different social networks enables a lot of strategic applications whose main strength is the integration of different communities that nevertheless preserves their diversity and autonomy. In this new scenario, the role of Social Network Analysis is crucial in studying the evolution of structures, individuals, interactions, and so on, and in extracting powerful knowledge from them. But the preliminary step to do is designing a good way to crawl the underlying graph. Although this aspect has been deeply investigated in the field of social networks, it is an open issue when moving towards SISs. Indeed, we cannot expect that a crawling strategy, specifically designed for social networks, is still valid in a Social Internetworking Scenario, due to its specific topological features. In this paper, we confirm the above claim, giving a strong motivation for our second contribution, which is the definition of a new crawling strategy. This strategy, specifically conceived for SISs, is shown to fully overcome the drawbacks of the state-of-the-art crawling strategies.

Recommendation of reliable users, social networks and high-quality resources in a Social Internetworking System

Social Internetworking Systems are a significantly emerging new reality; they group together some social networks and allow their users to share resources, to acquire opinions and, more in general, to interact, even if these users belong to different social networks and, therefore, did not previously know each other. In this context, owing to the huge dimension of existing social networks, the capability of a Social Internetworking System to provide its users with recommendations of reliable users and social networks, as well as of high-quality resources, is extremely relevant. In the past, user and resource recommendation has been investigated in the context of a single social network, whereas it has still received a little attention in the context of a Social Internetworking System, owing to the novelty of this phenomenon. For the same reason, social network recommendation has received an even less attention. In this paper we propose a trust-based approach to face these challenges. Specifically, we introduce a model to represent and handle trust and reputation in a Social Internetworking System and propose an approach that exploits these parameters to compute the reliability of a user or a social network, as well as the quality of a resource. These last measures are then exploited to perform recommendations.

Discovering missing me edges across social networks

Distinct social networks are interconnected via membership overlap, which plays a key role when crossing information is investigated in the context of multiple-social-network analysis. Unfortunately, users do not always make their membership to two distinct social networks explicit, by specifying the so-called me edge (practically, corresponding to a link between the two accounts), thus missing a potentially very useful information. As a consequence, discovering missing me edges is an important problem to address in this context with potential powerful applications. In this paper, we propose a common-neighbor approach to detecting missing me edges, which returns good results in real-life settings. Indeed, an experimental campaign shows both that the state-of-the-art common-neighbor approaches cannot be effectively applied to our problem and, conversely, that our approach returns precise and complete results.

Finding reliable users and social networks in a social internetworking system

Social internetworking systems are a significantly emerging new reality; they group together a set of social networks and allow their users to share resources, to acquire opinions and, more in general, to interact, even if these users belong to different social networks and, therefore, did not previously know each other. In this context the notions of trust and reputation play a very relevant role. These notions have been widely studied in the past in several contexts whereas they have been largely neglected in the social internetworking research; however, since this application field presents several peculiarities, the results found in other application contexts are not automatically valid here. This paper introduces a model to represent and handle trust and reputation in a social internetworking system and proposes an approach that exploits these parameters to provide users with suggestions about the most reliable persons they can contact or social networks they can register to.

Comparing Twitter and Facebook user behavior

We compare the behavior of users who belong to both Twitter and Facebook.We adopt a truly multi-OSN perspective by basing our analysis on membership overlap.We study overlapping friendship, user activity, degree, and privacy awareness. Understanding online-social-network (OSN) user behavior is an important challenge in the field of social network analysis, as OSNs play a significant role in peoples daily lives. So far, many studies considering only one OSN or, at most, comparing results obtained for a single OSN, have been provided. Nowadays, users typically join more OSNs and this is an important aspect that should be taken into account for user behavior analysis. In this paper, we give an important contribution in this direction, by analyzing the behavior of users belonging to both Facebook and Twitter. This way, the analysis is well-founded because it is conducted on a common set of users and, further, a number of specific analyses become possible (as common friendship). Our study is carried out on data extracted from the web, and allows us to find important specificities of these users about their privacy setting, the choice of friends and the activity they do, which are generally consistent with the recent findings in this field.

Crawling Social Internetworking Systems

In new generation social networks, we expect that the paradigm of Social Internetworking Systems (SISs, for short) will be more and more important. In this new scenario, the role of Social Network Analysis is of course still crucial but the preliminary step to do is designing a good way to crawl the underlying graph. While this aspect has been deeply investigated in the field of social networks, it is an open issue when moving towards SISs. Indeed, we cannot expect that a crawling strategy which is good for social networks, is still valid in a Social Internetworking Scenario, due to its specific topological features. In this paper, we first confirm the above claim and, then, define a new crawling strategy specifically conceived for SISs. Finally, we show that it fully overcomes the drawbacks of the state-of-the-art crawling strategies.

Driving Global Team Formation in Social Networks to Obtain Diversity

In this paper, we present a preliminary idea for a crowdsourcing application aimed at driving the process of global team formation to obtain diversity in the team. Indeed, it is well known that diversity is one of the key factors of collective intelligence in crowdsourcing. The idea is based on the identification of suitable nodes in social networks, which can profitably play the role of generators of diversity in the team formation process. This paper presents a first step towards the concrete definition of the above application consisting in the identification of an effective measure that can be used to select the most promising nodes w.r.t. the above feature.

A model to support design and development of multiple-social-network applications

Online social networks have become so pervasive in peoples lives that they can play a crucial role in design and development processes of applications. At moment, a gap exists w.r.t. standard networking programming to support social-network-based programming in large, according to software engineering principles of genericity and polymorphism. This drawback is made evident when applications should be built on top of multiple social networks and the user-centered vision should be kept. Indeed, heterogeneity of social networks does not allow us to produce software with suitable abstraction. In this paper, we cover the above gap by defining and implementing a model aimed at generalizing concepts, actions and relationships of existing social networks. The effectiveness of our approach is shown by two case studies.