Introduction to Social Network Analysis using UCInet and Netdraw

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1 Introduction to Social Network Analysis using UCInet and Netdraw Dr. oec. Ines Mergel The Program on Networked Governance Kennedy School of Government Harvard University

2 Tutorial Website

3 Content Assembling network data Representing relational data: Matrices & Sociograms Quantitative network analysis Network visualization as an analysis tool: Interpreting network diagrams List of online resources & classes Bibliography

4 Assembling a social network: Data collection Unit of observation: Entity on which measurements are taken = actor, from whom we elicit information about ties (actor, pair of actors, relational tie, or event) Modeling unit on a number of different levels: Actor, dyad, triad, subset of actor, or whole network Quantification of the relationship: - directional vs. nondirectional - dichotomous vs. valued

5 Assembling a social network: Data collection Surveys Collect perceptions of interactions List of names or free recall Free vs. fixed choices Ratings vs. complete rankings Observations Face-to-Face interactions: Who talks to whom at a party? Who answers to what kinds of requests on a listserver? Interviews Face-to-face, or telephone Example: Snowball principle: Who else is important in this network? Indirect data Archival records: past political interactions, co-authorship, court records, More reliable: without social ranking Avoid the risk of inaccurate recollections of respondent

6 Representing relational content Network data (Cartwright/Harary 956) Sociometric data: X = population under investigation represented in X x X table Actor-event matrix: X x Y table Arc/node list: ab, ae, ba, bc, bd, ce, dc, ea Board members Y Actor-event matrix Companies X Companies X Network graph: b d a e c Companies X

7 Sociograms (Moreno, 953) Sociometry : measurement of interpersonal relations in small groups Visual Exploration as an analysis method Representing the formal properties of social configurations Patterns through which perceptions of relationships are structured Useful for: Presenting structures of influence among community elites (Laumann/Pappi, 976) Corporate Interlocks (Levine, 972) Role structures in groups (Breiger et al., 975; Burt 976/982) Interaction patterns in small groups (Freeman et al. 989)

8 Sociometric Star -- F -- E -- D -- C -- B -- A F E D C B A Sociometric choices: Whom would you choose as a friend? A B E C D F

9 Interpreting a Network Diagram Nodes Relations Lines Arrows Positions Central Peripheral Isolate Groups/Subgroups Broker Coordinator Gatekeeper Representative

10 Network Connection Density Overall level of connection within a network Relative measure: Total number of ties/max. possible number of ties In UCInet: Network > Cohesion > Density Low density (25%) Average dist. = 2.27 High density (39%) Average dist. =.79

11 Network Connections (II) Distance Calculates the average distance to reach every actor in the network Shorter distance = fast reach, short paths, high connectivity In UCInet: Network > Cohesion > Distance Short average distance Long average distance

12 Centrality Measures Degree Centrality Number of incoming and outgoing ties (nominations) Indicates how well an actor is connected within the overall network (individual measure) Valued: Indegree/Outdegree; dichotomized: number of existing ties UCInet: Network Centrality > Degree degree = 7 Betweeness Centrality Extent to which an actor lies on the shortest path between other (two) other actors (adjusted by the number of alternative shortest paths) Mediates information, resource flow: liaison or gatekeeper UCInet: Network Centrality > Betweeness

13 Centrality Measures Closeness centrality Number of links/steps along shortest paths from the focal actor to any other actor Indicator of how quickly an information can reach an actor UCInet: Network > Centrality > Closeness MEGA Centrality Analysis in UCInet

14 Quantitative Data Analysis Data analysis decisions: Symmetrize relationships Minimum: only reciprocated ties Maximum: at least one tie makes a reciprocal relation Dichotomize /: absent or existing tie for alued data -> find meaningful cut point Data import: Excel -> import Type into UCINet spreadsheet Node list Network vs. attribute data

15 Visualization of Social Networks: Netdraw Centrality of d = 6

16 NetDraw: Sample Davis data. 2-Mode representation

17 Network drawing principles Distance between vertices expresses the strength or number of ties as closely as possible Example: distance between cities on a map = geographic distance Connected nodes are drawn closer together than nodes that are not related. Line length proportional to line values in case of lines with unequal values Don t lump nodes on top of each other or on top of lines they are not connected to Minimize crossing lines Choose automatic drawing mechanisms and then manipulate by hand or redraw several times (de Nooy/Mrvar/Batagelij 25)

18 Example: Network Visualization with Pajek Source: Pajek Homepage (http://research.lumeta.com/ches/map/gallery/index.html)

19 Example: Network Visualization with Pajek Colored by distance from the host Colored by toplevel domain name Colored by ISPs, the city-states of the Internet. Source: Pajek Homepage (http://research.lumeta.com/ches/map/gallery/index.html)

20 Example: Network Visualization with Mage Mage Java Mage Source:

21 Example: Network Visualization with Visone Analysis and visualization of social networks

22 Example: Network Visualization with Krackplot Circular layout Annealed layout

23 Relational content analysis: Information visualization Source: Prof. Ulrik Brandes, University of Constance (Germany)

24 Touchgraph: Visualization of Interrelated Information

25 Issue Crawler (govcom.org)

26 Grokker.com See New York Times article:

27 Online Resources GraphViz (Open Source Graph Drawing Program) ttp://www.graphviz.org/ iknow Inquiring Knowledge Networks on the Web: KrackPlot: Module Dynamic Visualization of Social Networks (MIT): Mage - 3D vector display program which shows "kinemage" graphics (http://kinemage.biochem.duke.edu/kinemage/kinemage.php) Netdraw: Pajek Program for Large Network Analysis: UCInet: Yahoo usergroup: Visone analysis and visualization of social networks Link collection of network visualization tools (especially for Internet data visualization):

28 Social Network Analysis Courses Center for Computational Analysis of Social and Organizational Systems (CASOS) Summer Institute directed by Kathleen M. Carley: The purpose of this institute is to provide an intense and hands-on introduction to dynamic network analysis and computational organization theory (Quote from CASOS website). Recent instructors: Carley, Krackhardt, Borgatti. Essex Summer School in Social Science Data Analysis and Collection (UK) Introduction to Social Network Analysis alternately taught by Steve Borgatti and Martin Everett (full syllabus including links to papers and other valuable online resource) at the University of Essex in England. Advanced Social Network Analysis taught by John Skvoretz at the University of Essex in England. ICPSR Interuniversity Consortium for Political and Social Research (University of Michigan, Ann Arbor) Summer Program in Quantitative Methods of Social Research Social Network Analysis: Theories and Methods, Stan Wasserman and Bernice Pescosolido (University of Indiana, Bloomington) Social Network Analysis: An Introduction, Stan Wasserman (Univerisity of Indiana, Bloomington) PolNet Summer School on the Analysis of Political and Managerial Networks Basic introduction into Social Network Analysis using UCInet and Visone, co-organized by the University of Konstanz (Germany) and Tilburg University (The Netherlands). Additional classes and colloquia in Social Networks and Network Analysis: For a comprehensive list of courses including syllabi see INSNA website.

29 Dr. Ines A. Mergel The Program on Networked Governance Kennedy School of Government Harvard University

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