Social Internetworking Scenario Research Articles

A Model of Information Diffusion in Interconnected Online Social Networks

Online social networks (OSN) have today reached a remarkable capillary diffusion. There are numerous examples of very large platforms people use to communicate and maintain relationships. People also subscribe to several OSNs, e.g., people create accounts on Facebook, Twitter, and so on. This phenomenon leads to online social internetworking (OSI) scenarios where users who subscribe to multiple OSNs are termed as bridges . Unfortunately, several important features make the study of information propagation in an OSI scenario a difficult task, e.g., correlations in both the structural characteristics of OSNs and the bridge interconnections among them, heterogeneity and size of OSNs, activity factors, cross-posting propensity, and so on. In this article, we propose a directed random graph-based model that is amenable to efficient numerical solution to analyze the phenomenon of information propagation in an OSI scenario; in the model development, we take into account heterogeneity and correlations introduced by both topological (correlations among nodes degrees and among bridge distributions) and user-related factors (activity index, cross-posting propensity). We first validate the model predictions against simulations on snapshots of interconnected OSNs in a reference scenario. Subsequently, we exploit the model to show the impact on the information propagation of several characteristics of the reference scenario, i.e., size and complexity of the OSI scenario, degree distribution and overall number of bridges, growth and decline of OSNs in time, and time-varying cross-posting users propensity.

A system for extracting structural information from Social Network accounts

The social network phenomenon involves hundreds of millions of people every day. This enormous volume of activity results in a huge source of information that can be valuable in many fields, for both research and application purposes. The relevance of this information strongly depends on the evolution occurring in the social Web, in which interaction among different social networks and their cross-relationships are becoming progressively more important. This, in fact, represents the basis of an emergent scenario called Social Internetworking Scenario. However, efficiently accessing and fruitfully querying this huge information source is not easy, because no tool to support applications needing a massive utilization of cross-social-network data exists. In this paper, we fill this gap by proposing Social Network Account Knowledge Extractor SNAKE, a system supporting the extraction of structural data from a social network account. SNAKE is implemented in such a way as to be easily integrated in any social-network-based application. To show the practical relevance of our proposal, we present our experience gained in three possible real-life applications strongly relying on information provided by SNAKE. Copyright © 2014 John Wiley & Sons, Ltd.

A conceptual framework for community detection, characterisation and membership in a social internetworking scenario

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 these problems, namely community detection, characterisation and membership in a social internetworking scenario. In order to face them, we must preliminarily investigate a further problem, i.e., user similarity detection.

A clustering-based analysis of a social internetworking scenario

Social network analysis is an emergent research stream aiming at studying the characteristics of social networks. Several methodologies have been developed to extract useful knowledge from single social networks, but little attention has been devoted to studying multi-social-network scenarios, whose importance is, by contrast, dramatically growing, because social internetworking systems may offer progressively more powerful and innovative services. In this scenario, we consider clustering as a fruitful point of view from which multi-social-network scenarios can be studied. Indeed, every social network includes distinguishing features and a clustering-based approach may highlight the differences among social networks, yet analysing information about the whole system. But when we apply clustering to a social internetworking scenario (SIS), several issues arise. In this paper, we address this problem, giving interesting hints on how clustering-based analysis should be conducted and analyse a real-life SIS obtaining meaningful and original 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.

A new ego network model and an approach to extracting an ego network compliant with this model from a social internetworking system

In this paper, we propose a new ego network model and an approach to extracting an ego network compliant with this model from a social internetworking system. Ego networks have been largely exploited in the past in the context of social network analysis where they referred to single social networks. Actually, we argue that they can also be profitably exploited in many application fields different from social network analysis, such as resource and user recommendation, extraction of hidden relationships among users and resources, definition of more cohesive communities. Moreover, they could refer to a set of social networks interacting with each other i.e., a social internetworking system, instead of to a single social network. In this paper, first we introduce a new model to represent a social internetworking system, and a new ego network model compliant with a social internetworking scenario and conceived to favour the exploitation of ego networks in several new application fields. Then, we describe our ego network extraction approach. After this, we illustrate some experiments devoted to test our approach. Finally, we examine some related approaches and highlight the similarities and the differences with our own.

Bridge analysis in a Social Internetworking Scenario

The rapid development of the number and the size of Online Social Networks (OSNs) makes the analysis of Social Internetworking Scenarios (SISs) extremely challenging. In a SIS, a user can join multiple OSNs and two users can interact with each other even though they joined different OSNs and did not know each other. While OSNs have been extensively studied in the last years, the most peculiar aspects of Social Internetworking Scenarios have not been yet investigated, especially from the Social Network Analysis perspective. Our paper tries to give a first important contribution in this field by deeply studying the core elements of a SIS, i.e., bridges. Bridges are those users who joined more OSNs and allow users of different OSNs to cooperate. We investigate the main features of this category of users by means of a Social Network Analysis campaign. In particular, we define several specific crawling strategies and extract several samples from a SIS by applying each of them. The experimental results define a clear “identikit” of bridges allowing us to state a number of non-trivial conclusions about their role in a SIS.

PHIS: A system for scouting potential hubs and for favoring their “growth” in a Social Internetworking Scenario

In this paper we propose ΦΦ (PHIS), a system for scouting current and potential hubs in a Social Internetworking Scenario (SIS) and for favoring the “growth” of these last ones in such a way that they can become real hubs quickly. ΦΦ operates on a SIS, rather than on a single social network. It uses a hypergraph-based model to represent the SIS. In order to verify if a user is a (current or potential) hub, it considers a range of criteria encompassing her reputation, her capability of receiving and delivering information, her position in the social networks of the SIS and the wideness of her profile. The same criteria are also exploited by ΦΦ to organize a campaign of stimulations to “train” a potential hub to become a real one.

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.