In this paper we aim at addressing the correlation between two critical factors in mobile social networks (MSNs): the social-relationship networking among users and the spatial mobility pattern of users. Specifically, we investigate the impact of users' spatial distribution on their social relationship formation in MSNs. Based on the geolocation data (check-in records) and social relation data of MSN users, we propose a model, called neighborhood-cardinality-based model (NCBM), to describe this impact by taking into account both the multiple home-points/hotspots property of spatial mobility and the long-tailed social relationship degree distribution of MSN users. We define a fundamental quantity for each user, i.e., the so-called neighborhood cardinality, to measure how many and how often other MSN users visit his nearby area with a given range. The core of NCBM is a principle: The probability that a user, say u, is followed by another user, say v, obeys a power law distribution of the neighborhood cardinality of user u. The proposed formation model is evaluated on two large check-in datasets: Brightkite and Gowalla. Our experimental results indicate that the proposed formation model provides a useful paradigm for capturing the correlation between MSN users' mobility patterns and social relationships.

In this paper, we aim to model the formation of interest-driven data dissemination in online social networks (OSNs). We focus on a usual type of interest-driven social sessions in OSNs, called Social-InterestCast, under which a user will autonomously determine whether to view the content from his followees depending on his interest. To figure out the formation mechanism of such a Social-InterestCast, we propose a four-layered system model, consisting of physical layer, social layer, content layer, and session layer to model this interestdriven sessions. To the best of our knowledge, this is the first work to model data dissemination in OSNs with the interestdriven characteristics.

In this paper, we study capacity scaling laws of the deterministic dissemination (DD) in random wireless networks under the generalized physical model (GphyM). This is truly not a new topic. Our motivation to readdress this issue is two-fold: Firstly, we aim to propose a more general result to unify the network capacity for general homogeneous random models by investigating the impacts of different parameters of the system on the network capacity. Secondly, we target to close the open gaps between the upper and the lower bounds on the network capacity in the literature. We derive the general upper bounds on the capacity for the arbitrary case of (λ, nd, ns) by introducing the Poisson Boolean model of continuum percolation, where λ, nd, and ns are the general node density, the number of destinations for each session, and the number of sessions, respectively. We prove that the derived upper bounds are tight according to the existing general lower bounds constructed in the literature.