Introduction: Applications of Social Network Analysis

AbstractSocial Network Analysis (SNA) has gained importance over the last two decades, both as a research program and toolbox for network analytical applications in various settings. The international conference on Applications of Social Network Analysis (ASNA) brings together network scholars from different interdisciplinary backgrounds and provides a forum to discuss latest research results and new applications. The contributions included in these proceedings are a selection of the papers from the 6 ASNA conference, which took place in Zurich, Switzerland, from 26–28 August 2009, and reflect the wide array of SNA applications presented and discussed every year at ASNA

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;

Analysis of Home Location Estimation with Iteration on Twitter Following Relationship

User's home locations are used by numerous social media applications, such as social media analysis. However, since the user's home location is not generally open to the public, many researchers have been attempting to develop a more accurate home location estimation. A social network that expresses relationships between users is used to estimate the users' home locations. The network-based home location estimation method with iteration, which propagates the estimated locations, is used to estimate more users' home locations. In this study, we analyze the function of network-based home location estimation with iteration while using the social network based on following relationships on Twitter. The results indicate that the function that selects the most frequent location among the friends' location has the best accuracy. Our analysis also shows that the 88% of users, who are in the social network based on following relationships, has at least one correct home location within one-hop (friends and friends of friends). According to this characteristic of the social network, we indicate that twice is sufficient for iteration.Comment: The 2016 International Conference on Advanced Informatics: Concepts, Theory and Application (ICAICTA2016

Health Applications of Social Network Analysis and Computational Social Science

Social network analysis has proliferated across the social and behavioral sciences, shifting our analytical focus from individuals to the patterns of social ties that connect them. This perspective has enriched our understanding of a great variety of health-related phenomena, including the spread of STDs on contact networks, the spread of health care practices on physicians’ professional networks, the dynamics of patient transfers on networks of clinics, and the spread of weight-related behaviors among adolescents at risk for obesity. The advent of the era of computational social science has augmented the contributions of this perspective, by moving beyond expensive and laborious methods of questionnaires and direct observation to incorporate new techniques of data collection and analysis. For example, these include analysis of electronic health records or other time-stamped communication traces among healthcare practitioners; streams of behavioral data from wearable sensors, location-aware devices, or electronic calendars; automated analysis of text in documents; and mapping networks of interaction by citations and collaboration in clinical research literatures. Whereas much of computational social science has offered new ways of monitoring health behavior and healthcare behavior, or for analyzing those data, a further contribution has been to directly analyze these social processes in system dynamics models, microsimulation, and agent-based models. These approaches allow for computational experiments that assist in predicting and interpreting outcomes from health interventions. This poster will highlight some of my recent and pending work in this domain, aiming to identify potential collaborators in UMCCTS for projects that involve social networks or computational social science

Diffusion of Latent Semantic Analysis as a Research Tool: A Social Network Analysis Approach

Latent Semantic Analysis (LSA) is a relatively new research tool with a wide range of applications in different fields ranging from discourse analysis to cognitive science, from information retrieval to machine learning and so on. In this paper, we chart the development and diffusion of LSA as a research tool using Social Network Analysis (SNA) approach that reveals the social structure of a discipline in terms of collaboration among scientists. Using Thomson Reuters’ Web of Science (WoS), we identified 65 papers with “Latent Semantic Analysis” in their titles and 250 papers in their topics (but not in titles) between 1990 and 2008. We then analyzed those papers using bibliometric and SNA techniques such as co-authorship and cluster analysis. It appears that as the emphasis moves from the research tool (LSA) itself to its applications in different fields, citations to papers with LSA in their titles tend to decrease. The productivity of authors fits Lotka’s Law while the network of authors is quite loose. Networks of journals cited in papers with LSA in their titles and topics are well connected

Gaming on and off the social graph: the social structure of Facebook games

Games built on Online Social Networks (OSNs) have become a phenomenon since 3rd party developer tools were released by OSNs such as Facebook. However, apart from their explosive popularity, little is known about the nature of the social networks that are built during play. In this paper, we present the findings of a network analysis study carried out on two Facebook applications, in comparison with a similar but stand-alone game. We found that games built both on and off a social graph exhibit similar social properties. Specifically, the distribution of player-to-player interactions decays as a power law with a similar exponent for the majority of players. For games built on the social network platform however, we find that the networks are characterised by a sharp cut-off, compared with the classically scale-free nature of the social network for the game not built on an existing social graph