Scott Feld, Purdue University “Social Network Theorizing Using Ideal Types” Friday, November 12, 2010 (audio and slides).
Scott L. Feld suggests that one approach to network theorizing is to identify a useful “ideal type,” carefully specify its defining properties, derive important implications of those properties, and consider how deviations from the defining properties affect the relevant implications. Professor Feld will illustrate this approach by considering the ideal type of a robust network hierarchy, distinguish it from other similar patterns (e.g. a transitive hierarchy or simple core-periphery), provide an empirical illustration, and consider some causes and consequence of this type of network pattern.
Scott L. Feld is Professor of Sociology (and Political Science) at Purdue University, and a Visiting Scholar at NICO. This presentation is part of his current effort to explicate the theoretical strategy in his earlier works on the focused organization of social ties, on friends of friends, and on the robust network hierarchies found in academia.
The presentation will provide an introduction to ET-V 1.0, the latest software released by Benjamin Elbirt / Elbirt Technologies. ET-V 1.0 is a visualization, animation and sonification application for understanding network data in multidimensional space. It is a successor to the Jacob’s Ladder (Elbirt, 2005 & 2009a & 2009b) software line. The software has been rebuilt to use XLS and XLSX files (Microsoft Excel) and includes many new features that improve the overall display and performance. This application is only limited by available resources (memory/CPU/GPU) for the data volume.
M2C 4.0 is provided for conversion of data to coordinate systems. M2C 4.0 is the latest Matrix to Coordinates conversion program that uses the latest MDSJ (Brandes & Pich, 2007) and a customized Procrustes rotation algorithm for longitudinal analysis. Inputs include pairs or matrix, text delimited or XLS/XLSX files. Outputs are in XLSX files and include various calculations and formats including ET-V. The presentation will begin with a brief description and discussion of the M2C application and how the data is converted from relational pairs, matrixes and other formats into a coordinate system of equivalences. This will be followed by a brief tour of ET-V, the data visualization program. Finally, three data sets will be loaded and displayed using various ET-V functionality. These data sets are 1) Migration Patterns among Canadian Provinces (Barnett & Sung, 2003) (73 time points, animated and intonated); 2) US Senate Bill Co-sponsorship (Fowler, 2006) (Fowler, Co-sponsorship Network Data Page, 2010) (17 time points, animated); and 3) Sunbelt Conference Co-authorship networks (Elbirt, 2010) (10 time points, animated).
Bio: Benjamin Elbirt is a software engineer who has developed internet and desktop applications for more than 12 years. He received his master’s degree from SUNY Buffalo under Dr. George Barnett, Dr. Joseph Woelfel and Dr. Frank Tutzauer in 2008. Benjamin is currently employed as the Chief Information Officer of INSNA – the International Network for Social Network Analysis. Areas of interest include Matrix Mathematics, Semantic Text Analysis, Data Visualization and Intonation, Longitudinal/Time Series data, Data Collection, Cognitive Modeling, Agent Based Modeling and Social Network Analysis. More information on software and publications can be found at his website http://www.elbirttechnologies.com.
Networks are a data structure common across all social media that allow populations to author collections of connections. The Social Media Research Foundation’s NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. Networks become as easy to create as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, and email networks will be presented.
Marc Smith is a sociologist specializing in the social organization of online communities and computer mediated interaction. He founded and managed the Community Technologies Group at Microsoft Research in Redmond, Washington and led the development of social media reporting and analysis tools for Telligent Systems. Smith leads the Connected Action consulting group and lives and works in Silicon Valley, California. Smith cofounded the Social Media Research Foundation, a nonprofit devoted to open tools, data, and scholarship related to social media research.
The concept of impersonal knowledge networks proposes a new approach for the study of political knowledge and public opinion. Factual knowledge questions and the analysis of simple frequency distributions provide only limited insights into the complex structures and dynamics of how people think and decide on political issues. The concept of impersonal knowledge networks proposes to understand public opinion as a network of topic related aspects. Free word associations of individuals are used as the basis to aggregate bigger knowledge networks. This aggregation is possible by transforming the two-mode network of respondents and their answers to a one mode network of topic aspects. Data presented in this talk were collected in representative CATI interviews on three different national referendum campaigns in Switzerland. The panel design allows not only to describe the knowledge structure at a given time point but also to analyze the dynamics of the structure.
Thomas N. Friemel is an Assistant Professor at the Institute of Mass Communication and Media Research at the University of Zurich (IPMZ) and currently Visiting Scholar at the Annenberg School for Communication at the University of Pennsylvania. He is the organizer of the annual conference on Applications of Social Network Analysis (ASNA) at the University of Zurich and ETH Zurich and applies SNA in various ways in mass communication research.
We propose an exchange platform where people lend each other objects of small value, such as star-shaped screwdrivers, which are transported by the owners or by carriers (friends or acquaintances of the owner in the social network). By running simulations on mobile call detail records, which include location information, from a large metropolitan area, we evaluate the performance of several transportation strategies. Results show that completely unoptimized routing heuristics could deliver an average of 3908 objects, over 10000 injected objects, with an average delivery time of 0.59 days. These preliminary results suggest that, under considerably general assumptions, social networks may indeed be an effective and inexpensive infrastructure for transportation networks. These initial results have important implications for sustainability.
Mauro Cherubini obtained a degree in Psychology and Education in 2001. He then worked at Media Lab Europe, in Ireland, with several study visits at MIT in Boston. In 2004, Mauro earned a Master of Arts by Research at Dublin City University. In 2008, he was conferred a PhD in Computer Science by the Swiss Federal Institute of Technology, where he conducted research on Human-Computer Interaction. In 2008, Mauro joined Telefonica Research, in Barcelona.
Both the way in which trust and advice relations influence the performance of employees in organizations, and the level at which these networks might have an impact on individual performance have been a major topic of discussion. This paper simultaneously investigates the impact of (1) trust as an independent effect next to advice, (2) trust as an underlying source for the emergence of advice, as well as (3) the importance of multiplex advice-trust ties for performance. Moreover a multilevel analysis is used to simultaneously study the position of members in a team, and the impact of the structural characteristics of the team as a whole for individual performance. The results at the individual level show that the impact of frequent advice giving on performance is only partially mediated by the level to which the person is considered more trustworthy, while the effect of advice relations embedded in a trust-relation (i.e. advice-trust multiplexity) is not important. However, at the team level results show not only that a centralized advice structure (one, or a few persons being asked a lot for advice) and a decentralized trust network (members being more similar in the level they trust others) both have a positive effect on performance, but also that having more advice relations which are combined with trust in the team as a whole, have an impact on the performance of all its members.
Filip Agneessens’ research centers on network formation within organizations and their impact on attitudes and behavior of individual employees, with a particular focus on how networks might impact job satisfaction and performance. He has also been working on the development of random and biased networks, the building of social support typologies and how they might impact an individual’s wellbeing. In recent work he has also adapted exponential random graph models (p*models) to study cultural participation and (co) sponsorship among senators as two-mode networks. He teaches courses on network theory, social network methods and social network analysis applied to organizations.