Important Network Measures: Chapter 5

NETWORK METRICS

Presentation based on Hansen, D., Shneiderman, B., & Smith, M. A. (2011). Analyzing Social Media Networks with NodeXl: Insights from a Connected World. New York, NY: Morgan Kaufmann

Please provide acknowledgement for use as follows: Kwon, H. (2013). “Social Network Analysis :Basics.” Lecture Presentation. Arizona State University

Quantitative results by analyzing relative structure of the whole networks and individuals’ (vertices) positions within a network

Two level of metricsOverall graph metrics (network as a

whole)Vertex-specific metrics (individual

within a network)

NETWORK METRICS

1. Density: Measures “How highly connected vertices are”

Density = # of edges/ # of all possible edges *** # of all possible edges =n(n-1)/2 ***

1. OVERALL GRAPH METRICS

Density? Density?

1. OVERALL GRAPH METRICS

2. Component:A cluster of vertices that are connected to each other but separate from other vertices in the graph

3. Isolate = a single vertex component

1. OVERALL GRAPH METRICS

2. Component:A cluster of vertices that are connected to each other but separate from other vertices in the graph

3. Isolate = a single vertex component

1. CentralityDegree: a count of the number of unique edges that

are connected to a given vertexBetweenness: a measure of how often a given vertex

lies on the shortest path (geodesic distance) between two other vertices. Higher betweenness centrality means that a vertex is positioned as a bridge (or gatekeeper) between many pairs of other vertices.

Closeness: the average distance between a vertex and every other vertex in the network. Higher closeness centrality means that a vertex has the shortest distance to all others.

Eigenvector Centrality: a measure of the value of connections that a given vertex has. If a vertex has connections to others with high degree centralities, the vertex shows high eigenvector centrality.

2. VERTEX-SPECIFIC METRICS

2. Clustering Coeffi cient: A measure of how a vertex’s friends are connected to one another. If my friends have connections to one another, I have a high clustering coeffi cient. If they are not connected, I have a low clustering coeffi cient.

Measures of Degree and Eigenvector Centralities diff er between un-weighted (whether there is a edge or not) and weighted (how valued the edge is) network.

2. VERTEX-SPECIFIC METRICS