Fast community structure local uncovering by independent vertex-centred process

This paper addresses the task of community detection and proposes a local approach based on a distributed list building, where each vertex broadcasts basic information that only depends on its degree and that of its neighbours. A decentralised external process then unveils the community structure. The relevance of the proposed method is experimentally shown on both artificial and real data.Comment: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Aug 2015, Paris, France. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Minin

Social relationship based routing for delay tolerant Bluetooth-enabled PSN communications

PhDOpportunistic networking is a concept derived from the mobile ad hoc networking in which devices have no prior knowledge of routes to the intended destinations. Content dissemination in opportunistic networks thus is carried out in a store and forward fashion. Opportunistic routing poses distinct challenges compared to the traditional networks such as Internet and mobile ad hoc networks where nodes have prior knowledge of the routes to the intended destinations. Information dissemination in opportunistic networks requires dealing with intermittent connectivity, variable delays, short connection durations and dynamic topology. Addressing these challenges becomes a significant motivation for developing novel applications and protocols for information dissemination in opportunistic networks. This research looks at opportunistic networking, specifically at networks composed of mobile devices or, pocket switched networks. Mobile devices are now accepted as an integral part of society and are often equipped with Bluetooth capabilities that allow for opportunistic information sharing between devices. The ad hoc nature of opportunistic networks means nodes have no advance routing knowledge and this is key challenge. Human social relationships are based on certain patterns that can be exploited to make opportunistic routing decisions. Targeting nodes that evidence high popularity or high influence can enable more efficient content dissemination. Based on this observation, a novel impact based neighbourhood algorithm called Lobby Influence is presented. The algorithm is tested against two previously proposed algorithms and proves better in terms of message delivery and delay. Moreover, unlike other social based algorithms, which have a tendency to concentrate traffic through their identified routing nodes, the new algorithm provides a fairer load distribution, thus alleviating the tendency to saturate individual nodes

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 Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking

Socially aware networking is an emerging research field that aims to improve the current networking technologies and realize novel network services by applying social network analysis (SNA) techniques. Conducting socially aware networking studies requires knowledge of both SNA and communication networking, but it is not easy for communication networking researchers who are unfamiliar with SNA to obtain comprehensive knowledge of SNA due to its interdisciplinary nature. This paper therefore aims to fill the knowledge gap for networking researchers who are interested in socially aware networking but are not familiar with SNA. This paper surveys three types of important SNA techniques for socially aware networking: identification of influential nodes, link prediction, and community detection. Then, this paper introduces how SNA techniques are used in socially aware networking and discusses research trends in socially aware networking