Properties of a random attachment growing network

In this study we introduce and analyze the statistical structural properties of a model of growing networks which may be relevant to social networks. At each step a new node is added which selects 'k' possible partners from the existing network and joins them with probability delta by undirected edges. The 'activity' of the node ends here; it will get new partners only if it is selected by a newcomer. The model produces an infinite-order phase transition when a giant component appears at a specific value of delta, which depends on k. The average component size is discontinuous at the transition. In contrast, the network behaves significantly different for k=1. There is no giant component formed for any delta and thus in this sense there is no phase transition. However, the average component size diverges for delta greater or equal than one half.Comment: LaTeX, 19 pages, 6 figures. Discussion section, comments, a new figure and a new reference are added. Equations simplifie

See you on Facebook: the effect of social networking on human interaction

This paper proposes an evolutionary framework to explore the dynamics of social interaction in an environment characterized by online networking and increasing pressure on time. The model shows how time pressure encourages the choice to develop social interactions also through online networking instead of relying exclusively on face to face encounters. Our findings suggest that the joint influence exerted by the reduction in leisure time and the new opportunities of participation offered by web-mediated communication may progressively lead a growing share of the population to adopt networking sites as an indispensable environment for the development of interpersonal relationships.internet, computer-mediated communication, social networking, online networks, Facebook, human interaction, social capital

Essays on Applied Network Theory.

Network economics is a fast growing area of study, with a lot of potential for addressing a wide variety of socio-economic phenomena. Networks literally permeate our social and economic lives. The unemployed find jobs using the information and assistance of their friends and relatives. Consumers benefit from the research of friends and family into new products. In medicine and other technical fields, professional networks shape research patterns. In all these settings, the well-being of participants depends on social, geographic, or trading relationships. The countless ways in which network structures affect our well-being make it critical to understand: (i) how network structures impact behavior, (ii) what can be done, in the way of design by policymakers, to improve systemic outcomes. This area of study, broadly called network economics, is at the heart of my research interests. In my dissertation I focus on three specific applications of network theory. The first application concerns networks in trade, where network structure represents the organization of trade agreements between countries. The second application deals with networks in financial market, and the network is used to model the structure of interbank exposures. Lastly, for the third application, I consider networks in labor markets and migration. In this context, the network represents the structure of social relations between people. Each of these applications of network analysis is addressed by one of three chapters in the thesis.Network analysis (Planning); Social networks -- Mathematical models; Social sciences -- Network analysis; Economics, Mathematical;

Growing networks of overlapping communities with internal structure

We introduce an intuitive model that describes both the emergence of community structure and the evolution of the internal structure of communities in growing social networks. The model comprises two complementary mechanisms: One mechanism accounts for the evolution of the internal link structure of a single community, and the second mechanism coordinates the growth of multiple overlapping communities. The first mechanism is based on the assumption that each node establishes links with its neighbors and introduces new nodes to the community at different rates. We demonstrate that this simple mechanism gives rise to an effective maximal degree within communities. This observation is related to the anthropological theory known as Dunbar's number, i.e., the empirical observation of a maximal number of ties which an average individual can sustain within its social groups. The second mechanism is based on a recently proposed generalization of preferential attachment to community structure, appropriately called structural preferential attachment (SPA). The combination of these two mechanisms into a single model (SPA+) allows us to reproduce a number of the global statistics of real networks: The distribution of community sizes, of node memberships and of degrees. The SPA+ model also predicts (a) three qualitative regimes for the degree distribution within overlapping communities and (b) strong correlations between the number of communities to which a node belongs and its number of connections within each community. We present empirical evidence that support our findings in real complex networks.Comment: 14 pages, 8 figures, 2 table

A Trust-Based Relay Selection Approach to the Multi-Hop Network Formation Problem in Cognitive Radio Networks

One of the major challenges for today’s wireless communications is to meet the growing demand for supporting an increasing diversity of wireless applications with limited spectrum resource. In cooperative communications and networking, users share resources and collaborate in a distributed approach, similar to entities of active social groups in self organizational communities. Users’ information may be shared by the user and also by the cooperative users, in distributed transmission. Cooperative communications and networking is a fairly new communication paradigm that promises significant capacity and multiplexing gain increase in wireless networks. This research will provide a cooperative relay selection framework that exploits the similarity of cognitive radio networks to social networks. It offers a multi-hop, reputation-based power control game for routing. In this dissertation, a social network model provides a humanistic approach to predicting relay selection and network analysis in cognitive radio networks

Rethinking the patient: using Burden of Treatment Theory to understand the changing dynamics of illness

<b>Background</b> In this article we outline Burden of Treatment Theory, a new model of the relationship between sick people, their social networks, and healthcare services. Health services face the challenge of growing populations with long-term and life-limiting conditions, they have responded to this by delegating to sick people and their networks routine work aimed at managing symptoms, and at retarding - and sometimes preventing - disease progression. This is the new proactive work of patient-hood for which patients are increasingly accountable: founded on ideas about self-care, self-empowerment, and self-actualization, and on new technologies and treatment modalities which can be shifted from the clinic into the community. These place new demands on sick people, which they may experience as burdens of treatment.<p></p> <b>Discussion</b> As the burdens accumulate some patients are overwhelmed, and the consequences are likely to be poor healthcare outcomes for individual patients, increasing strain on caregivers, and rising demand and costs of healthcare services. In the face of these challenges we need to better understand the resources that patients draw upon as they respond to the demands of both burdens of illness and burdens of treatment, and the ways that resources interact with healthcare utilization.<p></p> <b>Summary</b> Burden of Treatment Theory is oriented to understanding how capacity for action interacts with the work that stems from healthcare. Burden of Treatment Theory is a structural model that focuses on the work that patients and their networks do. It thus helps us understand variations in healthcare utilization and adherence in different healthcare settings and clinical contexts