First steps in social network analysis
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First steps in social network analysis

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Zinayida Petrushyna, Ralf Klamma

Zinayida Petrushyna, Ralf Klamma

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First steps in social network analysis First steps in social network analysis Presentation Transcript

  • First steps in social network Zina Petrushyna analysis Ralf Klamma Workshop Terchova, June 2009 Zinayida Petrushyna, Ralf Klamma Chair for Information Systems and Databases, RWTH Aachen University, Germany Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke Joint TEL SummerSchool- June09-1
  • Motivation Old theories New theory • Actual process •Knowledge is continual Zina Petrushyna Ralf Klamma • Learning happens inside • Cognitive operations are done by machines • Instructional design • Network pedagogy pedagogy Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke Joint TEL SummerSchool- June09-2
  • Fundamentals A network is a graph (consists of nodes and edges) L1 L2 Examples: Zina Petrushyna • People and interactions between them L1 Ralf Klamma L2 • Websites and links Teacher • Cities and traffic connections Edges are Teacher • directed/undirected L3 L3 L4 L4 • multiple • weighted/unweighted Teacher Lehrstuhl Informatik 5 Two nodes are neighbors or adjacent when one (Information Systems) Prof. Dr. M. Jarke Joint TEL edge exist between two given vertices Teacher SummerSchool- June09-3
  • Fundamentals A path is a set of connected edges A length of a path is number of edges on the path Zina A distance of a path is a sum of the weights of the edges Petrushyna on the path A cycle is a path with repeated vertices Ralf Klamma A subgraph is a part of graph Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke Joint TEL SummerSchool- June09-4
  • Network Characteristics: Degree centrality Degree of a vertex: number of incoming and outgoing edges L1 Zina • in-degree L2 Petrushyna Ralf Klamma • out-degree Teacher • Simplest centrality measure L3 • A measure in some sense L4 shows the popularity of an actor Teacher Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke Joint TEL SummerSchool- June09-5
  • Network Characteristics: Closeness How far a node is from the others? Zina Petrushyna The closeness of the node i is defined as: c(i ) ≡ Ralf Klamma 1 ∑ j∈Nd (i , j ) Who are leaders? Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke Joint TEL SummerSchool- June09-6
  • Network Characteristics: Shortest-path Zina Petrushyna Ralf Klamma News, rumor, fad, message – does it know the ideal route? To get from one place to another more likely a message wanders around more randomly, encountering who it will. Certainly it is possible for information to flow between two individuals via a third mutual acquaintance, even when the Lehrstuhl Informatik 5 (Information Systems) two individuals in question are themselves well acquainted Prof. Dr. M. Jarke Joint TEL SummerSchool- June09-7
  • Network Characteristics: Betweenness Measure for the influence an actor can exert The shortest-paths v(j, k) for each j, k and j≠k the betweenness of node i is Zina Petrushyna Ralf Klamma vi ( j, k ) bi ≡ ∑ j ≠k v( j, k ) Who controls the flow of information? Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke Joint TEL SummerSchool- June09-8
  • Networ Measures Closeness and Betweenness Degree Ego-centric measure defining a node community Communication activity Betweenness Zina Petrushyna Measure of the extent to which a node lies on the Community control Ralf Klamma paths between others Closeness Measure of how long it will take information to spread Depedence, consideretely from a given node to others in the network efficiency Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke Joint TEL SummerSchool- June09-9
  • Examples Zina Petrushyna Ralf Klamma Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke Joint TEL SummerSchool- June09-10