SPoT: Representing the Social, Spatial, and Temporal Dimensions of Human Mobility with a Unifying Framework

Modeling human mobility is crucial in the analysis and simulation of opportunistic networks, where contacts are exploited as opportunities for peer-topeer message forwarding. The current approach with human mobility modeling has been based on continuously modifying models, trying to embed in them the mobility properties (e.g., visiting patterns to locations or specific distributions of inter-contact times) as they came up from trace analysis. As a consequence, with these models it is difficult, if not impossible, to modify the features of mobility or to control the exact shape of mobility metrics (e.g., modifying the distribution of inter-contact times). For these reasons, in this paper we propose a mobility framework rather than a mobility model, with the explicit goal of providing a exible and controllable tool for modeling mathematically and generating simulatively different possible features of human mobility. Our framework, named SPoT, is able to incorporate the three dimensions - spatial, social, and temporal - of human mobility. The way SPoT does it is by mapping the different social communities of the network into different locations, whose members visit with a configurable temporal pattern. In order to characterize the temporal patterns of user visits to locations and the relative positioning of locations based on their shared users, we analyze the traces of real user movements extracted from three location-based online social networks (Gowalla, Foursquare, and Altergeo). We observe that a Bernoulli process effectively approximates user visits to locations in the majority of cases and that locations that share many common users visiting them frequently tend to be located close to each other. In addition, we use these traces to test the exibility of the framework, and we show that SPoT is able to accurately reproduce the mobility behavior observed in traces. Finally, relying on the Bernoulli assumption for arrival processes, we provide a throughout mathematical analysis of the controllability of the framework, deriving the conditions under which heavy-tailed and exponentially-tailed aggregate inter-contact times (often observed in real traces) emerge

A fast algorithm for predicting links to nodes of interest

The problem of link prediction has recently attracted considerable attention in various domains, such as sociology, anthropology, information science, and computer science. In many real world applications, we must predict similarity scores only between pairs of vertices in which users are interested, rather than predicting the scores of all pairs of vertices in the network. In this paper, we propose a fast similarity-based method to predict links related to nodes of interest. In the method, we first construct a sub-graph centered at the node of interest. By choosing the proper size for such a sub-graph, we can restrict the error of the estimated similarities within a given threshold. Because the similarity score is computed within a small sub-graph, the algorithm can greatly reduce computation time. The method is also extended to predict potential links in the whole network to achieve high process speed and accuracy. Experimental results on real networks demonstrate that our algorithm can obtain high accuracy results in less time than other methods can

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Detecting Tie Strength from Social Media Data in a Conference Setting

The concept of tie strength was introduced by Granovetter as “a (probably linear) combination of the amount of time, the emotional intensity, the intimacy (mutual confiding), and the reciprocal services which characterize the tie”. Since the publication of this seminal study, several studies have been conducted incorporating the concept of tie strength in numerous fields. The growing rise of social media in recent years has shaped a new way of establishing and maintaining ties between people. As a result, studies have been conducted that, based on social media data, are focused on the evaluation of tie strength between users. Social media has also positioned itself as a key tool in the development of events such as conferences, as it is consolidated as the communication platform through which to disseminate information and knowledge and networking. Therefore, in the present study, it is sought to evaluate tie strength using publicly available Twitter data in the context of a conference. Specifically, the aim is to analyse the potential of implicit networks (particularly, mentions networks) generated in social media sites (particularly, Twitter) when evaluating tie strength and social ties, with special emphasis on weak ties and latent ties. Ultimately, the aim is to obtain conclusions that result in the demonstration of the utility and the advantages of implementing this analysis in the recommendation systems in conferences. To address the main statement problem, this study starts with a review of the existing literature related to the topic. Subsequently, as regards the empirical part of the study, a case study approach is conducted. Specifically, a longitudinal single-case analysis is analysed, since the mentions networks generated from the publicly available Twitter data of the conference HICSS along nine editions (from 2010 to 2018) are studied. Different measures of social network analysis have been used to obtain results and conclusions. Based on the analysis, different potentially useful measures for the evaluation of mentions networks and social ties are identified. These measures have served to analyse the social structures formed in a conference setting (highlighting star structures that reflect the information disseminating role of certain nodes), to identify the most relevant and influential participants (which generally correspond to important roles of the conference, as organizers or speakers), or to observe tendencies and groupings in communities according to common interests, among others