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How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the returnof migrants.

For decades, researchers have been trying to understand how people form their opinions. This quest has become even more pressing with the widespread usage of online social networks and social media, which seem to amplify the already existing phenomenon of polarization. In this work, we study the problem of polarization assuming that opinions evolve according to the popular Friedkin–Johnsen (FJ) model. The FJ model is one of the few existing opinion dynamics models that has been validated on small/medium-sized social groups. First, we carry out a comprehensive survey of the FJ model in the literature (distinguishing its main variants) and of the many polarization metrics available, deriving an invariant relation among them. Second, we derive the conditions under which the FJ variants are able to induce opinion polarization in a social network, as a function of the social ties between the nodes and their individual susceptibility to the opinion of others. Third, we discuss a methodology for finding concrete opinion vectors that are able to bring the network to a polarized state. Finally, our analytical results are applied to two real social network graphs, showing how our theoretical findings can be used to identify polarizing conditions under various configurations.

The widespread diffusion of Online Social Networks has given unforeseen opportunities for their users to share contents and mutually interact. However, current platforms offer inadequate guarantees as far as concerns the privacy of their users. To address these issues and leave to the users more control on their data, several recent proposals suggest to decentralize the storage of social data, aiming at leaving their control entirely to their owners. This approach has lead to the definition of Decentralized Online Social Networks (DOSNs), ranging from completely decentralized solutions, i.e. P2P solutions, to hybrid systems integrating external and private resources for storing user data. While DOSNs allow users to have more control over their data, they raise new challenges concerning the management and availability of social data. Guaranteeing data availability in a highly dynamic environment, defining proper algorithms for information diffusion and guaranteeing data privacy in a distributed setting are currently open problems in this area.

Social Searchresearch studies methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users’ preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users’ social profiles and behaviours (as they can be inferred from data available on social platforms) to optimize their information needs further.

In opportunistic networks, putting devices in energy-saving mode is crucial to preserve their battery, and hence to increase the lifetime of the network and foster user participation. A popular strategy for energy saving is duty cycling. However, when in energy-saving mode, users cannot communicate with each other. The side effects of duty cycling are twofold. On the one hand, duty cycling may reduce the number of usable contacts for delivering messages, increasing intercontacttimes, and delays. On the other hand, duty cycling may break long contacts into smaller contacts, thus also reducing the capacity of the opportunistic network. Despite the potential serious effects, the role played by duty cycling in opportunistic networks has been often neglected in the literature. In order to fill this gap, in this article, we propose a general model for deriving the pairwise contact and intercontact times measured when a duty cycling policy is superimposed on the original encounter process determined only by node mobility. The model we propose is general, i.e., not bound to a specific distribution of contact and intercontact times, and very accurate, as we show exploiting two traces of real human mobility for validation. Using this model, we derive several interesting results about the properties of measured contact and intercontact times with duty cycling: their distribution, how their coefficient of variation changes depending on the duty cycle value, and how the duty cycling affects the capacity and delay of an opportunistic network. The applicability of these results is broad, ranging from performance models for opportunistic networks that factor in the duty cycling effect, to the optimisation of the duty cycle to meet a certain target performance.

Ego networks models describe the social relationships of an individual (ego) with its social peers (alters). The structural properties of ego networks are known to determine many aspects of the human social behavior, such as willingness to cooperate and share resources. Due to their importance, we have investigated if Online Social Networks fundamentally change the structures of human ego networks or not. In this paper we provide a comprehensive and concise compilation of the main results we have obtained through this analysis. Specifically, by analysing several datasets in Facebook and Twitter, we have found that OSN ego networks show the same qualitative and quantitative properties of human ego networks in general, and therefore that, somewhat counter-intuitively, OSNs are just “yet another” social communication means which does not change the fundamental properties of personal social networks. Moreover, in this paper we also survey the main results we have obtained studying the impact of ego network structures on information diffusion in OSNs. We show that, by considering the structural properties of ego networks, it is possible to accurately model information diffusion both over individual social links, as well at the entire network level, i.e., it is possible to accurately model information “cascades”. Moreover, we have analyzed how trustedinformation diffuses in OSNs, assuming that the tie strength between nodes (which, in turn, determines the structure of ego networks) is a good proxy to measure the reciprocal trust. Interestingly, we have shown that not using social links over a certain level of trust drastically limits information spread, up to only 3% of the nodes when only very strong ties are used. However, inserting even a single social relationship per ego, at a level of trust below the threshold, can drastically increase information diffusion. Finally, when information diffusion is driven by trust, the average length of shortest paths is more than twice the one obtained when all social links can be used for dissemination. Other analyses in the latter case have highlighted that also in OSNs users are separated by about 6 (or less) degrees of separation. Our results show that when we need trustworthy “paths” to communicate in OSNs, we are more than twice as far away from each other.

Allowing mobile users to find and access resources available in the surrounding environment opportunistically via their smart devices could enable them to create and use a rich set of services. Such services can go well beyond what is possible for a mobile phone acting alone. In essense, access to diverse resources such as raw computational power, social networking relationships, or sensor readings across a set of different devices calls for distributed task execution. In this paper, we discuss the SCAMPI architecture designed to support distributed task execution in opportunistic pervasive networks. The key elements of the architecture include leveraging human social behavior for efficient opportunistic interaction between a variety of sensors, personal communication devices and resources embedded in the local environment. The SCAMPI architecture abstracts resources as service components following a service-oriented model. This enables composing rich applications that utilize a collection of service components available in the environment.

Ego networks have proved to be a valuable tool for understanding the relationships that individuals establish with their peers, both in offline and online social networks. Particularly interesting are the cognitive constraintsassociated with the interactions between the ego and the members of their ego network, which limit individuals to maintain meaningful interactions with no more than 150 people, on average, and to arrange such relationships along concentric circles of decreasing engagement. In this work, we focus on the ego networks of journalists on Twitter, considering 17 different countries, and we investigate whether they feature the same characteristics observed for other relevant classes of Twitter users, like politicians and generic users. Our findings are that journalists are generally more active and interact with more people than generic users, regardless of their country. Their ego network structure is very aligned with reference models derived in anthropology and observed in general human ego networks. Remarkably, the similarity is even higher than the one of politicians and generic users ego networks. This may imply a greater cognitive involvement with Twitter for journalists than for other user categories. From a dynamic perspective, journalists have stable short-term relationships that do not change much over time. In the longer term, though, ego networks can be pretty dynamic, especially in the innermost circles. Moreover, the ego-alter ties of journalists are often information-driven, as they are mediated by hashtags both at their inception and during their lifetime. Finally, we found that relationships between journalists are assortative in popularity: journalists tend to engage with other journalists of similar popularity, in all layers but especially in their innermost ones. Instead, when journalists interact with generic users, this assortativity is only present in the innermost layers.

In opportunistic networks, end-to-end communication among users does not require a continuous end-to-end path between source and destination. Network protocols are designed to be extremely resilient to events such as long partitions, node disconnections, etc. which are very features of this type of self-organising ad hoc networks. This is achieved by temporarily storing messages at intermediate nodes, waiting for future opportunities to forward them towards the destination. Clearly, designing routing and forwarding schemes is one of the main challenges in this environment. In this article, we provide a survey of the main approaches to routing in purely infrastructure-less opportunistic networks, by classifying protocols based on the amount of context information they exploit. Then, we provide an extensive quantitative comparison between representatives of protocols that do not use any context information, and protocols that manage and exploit a rich set of context information. Mainly, we focus on the suitability of protocols to adapt to the dynamically changing network features, as resulting from the user movement patterns that are driven by their social behaviour. Our results show that context aware routing is extremely adaptive to dynamic networking scenarios, and, with respect to protocols that do not use any context information, is able to provide similar performance in terms of delay and loss rate, by using just a small fraction of the network resources.

The aim of the MobiOpp workshop series is to bring together the (increasing) community of researchers working in the area of Mobile Opportunistic Networking. The first workshop in this series, MobiOpp'07, was held on June 11, 2007 in San Juan, Puerto Rico (USA) in conjunction with MobiSys 2007. MobiOpp'07 was sponsored by ACM SIGMOBILE and jointly organized by the IIT Institute of the Italian National Research Council (CNR) and by the UCLA Network Research Laboratory.