A Generalized Fractional Program for Maximizing Content Popularity in Online Social Networks

International audienceIn this paper, we consider a "generalized" fractional program in order to solve a popularity optimization problem in which a source of contents controls the topics of her contents and the rate with which posts are sent to a time line. The objective of the source is to maximize its overall popularity in an Online Social Network (OSN). We propose an efficient algorithm that converges to the optimal solution of the Popularity maximization problem

Modeling and analyzing device-to-device content distribution in cellular networks

Device-to-device (D2D) communication is a promising approach to optimize the utilization of air interface resources in 5G networks, since it allows decentralized proximity-based communication. To obtain caching gains through D2D, mobile nodes must possess content that other mobiles want. Thus, devising intelligent cache placement techniques are essential for D2D. The goal of this dissertation is to provide randomized spatial models for content distribution in cellular networks by capturing the locality of the content, and additionally, to provide dynamic content placement algorithms exploiting the node configurations. First, a randomized content caching scheme for D2D networks in the cellular context is proposed. Modeling the locations of the devices as a homogeneous Poisson Point Process (PPP), the probability of successful content delivery in the presence of interference and noise is derived. With some idealized modeling aspects, i.e., given that (i) only a fraction of users to be randomly scheduled at a given time, and (ii) the request distribution does not change over time, it has been shown that the performance of caching can be optimized by smoothing out the request distribution, where the smoothness of the caching distribution is mainly determined by the path loss exponent, and holds under Rayleigh, Ricean and Nakagami fading models. Second, to take the randomized caching model a step further, a spatially correlated content caching scenario is contemplated. Inspired by the Matérn hard-core point process of type II, which is a first-order pairwise interaction model, D2D nodes caching the same file are never closer to each other than the exclusion radius. The exclusion radius plays the role of a substitute for caching probability. The optimal exclusion radii that maximize the hit probability can be determined by using the request distribution and cache memory size. Unlike independent content placement, which is oblivious to the geographic locations of the nodes, the new strategy can be effective for proximity-based communication even when the cache size is small. Third, an auction-aided Matérn carrier sense multiple access (CSMA) policy that considers the joint analysis of scheduling and caching is studied. The auction scheme is distributed. Given a cache configuration, i.e., the set of cached files in each user at a given snapshot, each D2D receiver determines the value of its request, by bidding on the set of potential transmitters in its communication range. The values of the receiver bids are reported to the potential transmitter, which computes the cumulated sum of these variables taken on all users in its cell. The potential transmitter then reports the value of the bid sum to other potential transmitters in its contention range. Given the accumulated bids of all potential transmitters, the contention range and the medium access probability, a fraction of the potential transmitters are jointly scheduled, determined by the auction policy, in order to optimize the throughput. Later, a Gibbs sampling-based cache update strategy is proposed to iteratively optimize the hit rate by taking the scheduling scheme into account. In this dissertation, a variety of distributed algorithms for D2D content caching are proposed. Our results indicate that the geographic locality and the network parameters have a significant role in determining and optimizing the placement strategy. Exploiting the user interactions and spatial diversity, and incentivizing cooperation among D2D nodes are crucial in realizing the full potential of caching. Furthermore, from a network point of view, the scheduling and the caching phases are closely linked to each other. Hence, understanding the interaction between these two phases helps develop novel dynamic caching strategies capturing the temporal and spatial locality of the demand.Electrical and Computer Engineerin

Modeling and predicting time series of social activities with fat-tailed distributions

Fat-tailed distributions, characterized by the relation P(x) ∝ x^{−α−1}, are an emergent statistical signature of many complex systems, and in particular of social activities. These fat-tailed distributions are the outcome of dynamical processes that, contrary to the shape of the distributions, is in most cases are unknown. Knowledge of these processes’ properties sheds light on how the events in these fat tails, i.e. extreme events, appear and if it is possible to anticipate them. In this Thesis, we study how to model the dynamics that lead to fat-tailed distributions and the possibility of an accurate prediction in this context. To approach these problems, we focus on the study of attention to items (such as videos, forum posts or papers) in the Internet, since human interactions through the online media leave digital traces that can be analysed quantitatively. We collected four sets of time series of online activity that show fat tails and we characterize them. Of the many features that items in the datasets have, we need to know which ones are the most relevant to describe the dynamics, in order to include them in a model; we select the features that show high predictability, i.e. the capacity of realizing an accurate prediction based on that information. To quantify predictability we propose to measure the quality of the optimal forecasting method for extreme events, and we construct this measure. Applying these methods to data, we find that more extreme events (i.e. higher value of activity) are systematically more predictable, indicating that the possibility of discriminate successful items is enhanced. The simplest model that describes the dynamics of activity is to relate linearly the increment of activity with the last value of activity recorded. This starting point is known as proportional effect, a celebrated and widely used class of growth models in complex systems, which leads to a distribution of activity that is fat-tailed. On the one hand, we show that this process can be described and generalized in the framework of Stochastic Differential Equations (SDE) with Normal noise; moreover, we formalize the methods to estimate the parameters of such SDE. On the other hand, we show that the fluctuations of activity resulting from these models are not compatible with the data. We propose a model with proportional effect and Lévy-distributed noise, that proves to be superior describing the fluctuations around the average of the data and predicting the possibility of an item to become an extreme event. However, it is possible to model the dynamics using more than just the last value of activity; we generalize the growth models used previously, and perform an analysis that indicates that the most relevant variable for a model is the last increment in activity. We propose a new model using only this variable and the fat-tailed noise, and we find that, in our data, this model is superior to the previous models, including the one we proposed. These results indicate that, even if present, the relevance of proportional effect as a generative mechanism for fat-tailed distributions is greatly reduced, since the dynamical equations of our models contain this feature in the noise. The implications of this new interpretation of growth models to the quantification of predictability are discussed along with applications to other complex systems

Navigating the Range of Statistical Tools for Inferential Network Analysis

The last decade has seen substantial advances in statistical techniques for the analysis of network data, as well as a major increase in the frequency with which these tools are used. These techniques are designed to accomplish the same broad goal, statistically valid inference in the presence of highly interdependent relationships, but important differences remain between them. We review three approaches commonly used for inferential network analysis—the quadratic assignment procedure, exponential random graph models, and latent space network models—highlighting the strengths and weaknesses of the techniques relative to one another. An illustrative example using climate change policy network data shows that all three network models outperform standard logit estimates on multiple criteria. This article introduces political scientists to a class of network techniques beyond simple descriptive measures of network structure, and it helps researchers choose which model to use in their own research

A game theoretic framework for controlling the behavior of a content seeking to be popular on social networking sites

Over the years, people are becoming more dependent on Online Social Networks, through whom they constitute various sorts of relationships. Furthermore, such areas present spaces of interaction among users; they send more messages and posts showing domains they are interested in to guarantee the level of their popularity. This popularity depends on its own rate, the number of comments the posted topic gets but; also on the cost a user has to pay to accomplish his task on this network. However, the selfish behavior of those subscribers is the root cause of competition over popularity among those users. In this paper, we aim to control the behavior of a social networks users who try their best to increase their popularity in a competitive manner. We formulate this competition as a non-cooperative game. We porpose an efficient game theoretical model to solve this competition and find a situation of equilibrium for the said game

Improving Energy Efficiency and Security for Pervasive Computing Systems

Pervasive computing systems are comprised of various personal mobile devices connected by the wireless networks. Pervasive computing systems have gained soaring popularity because of the rapid proliferation of the personal mobile devices. The number of personal mobile devices increased steeply over years and will surpass world population by 2016.;However, the fast development of pervasive computing systems is facing two critical issues, energy efficiency and security assurance. Power consumption of personal mobile devices keeps increasing while the battery capacity has been hardly improved over years. at the same time, a lot of private information is stored on and transmitted from personal mobile devices, which are operating in very risky environment. as such, these devices became favorite targets of malicious attacks. Without proper solutions to address these two challenging problems, concerns will keep rising and slow down the advancement of pervasive computing systems.;We select smartphones as the representative devices in our energy study because they are popular in pervasive computing systems and their energy problem concerns users the most in comparison with other devices. We start with the analysis of the power usage pattern of internal system activities, and then identify energy bugs for improving energy efficiency. We also investigate into the external communication methods employed on smartphones, such as cellular networks and wireless LANs, to reduce energy overhead on transmissions.;As to security, we focus on implantable medical devices (IMDs) that are specialized for medical purposes. Malicious attacks on IMDs may lead to serious damages both in the cyber and physical worlds. Unlike smartphones, simply borrowing existing security solutions does not work on IMDs because of their limited resources and high requirement of accessibility. Thus, we introduce an external device to serve as the security proxy for IMDs and ensure that IMDs remain accessible to save patients\u27 lives in certain emergency situations when security credentials are not available

A Multi-Agent Modeling Social Network Analysis of Cooperative Learning Groups Within a Simulated Adult Education Classroom Learning Environment

Illiteracy and a lack of a high school diploma are impediments to a fulfilled and meaningful life. Low or reduced literacy and non-attainment of a high school diploma are significant problems in the United States. Adult education can be a vehicle to address these ever-present issues. A disproportionate number of students in adult education are minorities, members of lower socioeconomic statuses and traditionally marginalized groups who lack effective literacy skills and/or a high school diploma. Adult education and its related entities can serve as a vehicle to address these pervasive issues, but adult education as a program type is a field that has not been thoroughly researched. Given the extreme variance in the constituency of many adult education classrooms and the volatile nature of many adult learners’ intrinsic and extrinsic situations, research is limited and effective classroom practices specific to adult education are not well understood. Understanding the nature of the adult education classroom and the student networks within them may provide a better understanding of the complexities of the adult education classroom which, in turn, should engender further research and a better understanding of what types of cooperative learning environments and paradigms work best for adult learners. Social network analysis can assist in learning about the composition and connectivity of student learning groups and the formation of cooperative learning practices which has been shown to promote positive student outcomes. In an ever-changing classroom setting, where open enrollment is the standard, the role of incumbents versus newcomers to the adult education class in creating and maintaining student groups sheds light on how student groups can evolve and affect positive student outcomes both in the classroom and in the outside world