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Who Dunnit: The Party Mystery Game for Analyzing Network Structure and Information Flow Cover

Who Dunnit: The Party Mystery Game for Analyzing Network Structure and Information Flow

Open Access
|Oct 2019

Figures & Tables

Figure 1:

Nodelist and plot of a 14-player network. The plot should be displayed during debriefing.
Nodelist and plot of a 14-player network. The plot should be displayed during debriefing.

Figure 2:

Plots with centrality measures for the 14-player network. Node size is adjusted by each of the four centrality measures. Instructors can show these figures to students after analyzing the network structure.
Plots with centrality measures for the 14-player network. Node size is adjusted by each of the four centrality measures. Instructors can show these figures to students after analyzing the network structure.

Figure 3:

Nodelist and plot of a 28-player network. The plot should be displayed during debriefing.
Nodelist and plot of a 28-player network. The plot should be displayed during debriefing.

Figure 4:

Plots with centrality measures for the 28-player network. Node size is adjusted by each of the four centrality measures. Instructors can show these figures to students after analyzing the network structure.
Plots with centrality measures for the 28-player network. Node size is adjusted by each of the four centrality measures. Instructors can show these figures to students after analyzing the network structure.

Node centrality measures in the 28-player network_

Node IDDegree centralityCloseness centralityBetweenness centralityEigenvector centrality
150.010648.08330.7109
2 8 0.01185.66670.9748
310.009900.0402
460.012764.8333 1
530.010100.6345
640.011846.41670.5917
740.011935.33330.7543
85 0.0137 190.33330.6349
930.0133 191 0.1731
1060.0123185.50.0694
1140.0104110.50.0276
1240.01016.50.0337
1320.008500.2267
1420.009400.024
1530.0086460.0082
1610.009300.0161
1720.00995.250.2759
1820.0085230.0075
1930.00853.08330.2643
2040.007327.50.0046
2120.00935.41670.199
2220.009400.024
2320.007200.003
2410.006100.0011
2520.010814.58330.2118
2610.008500.2266
2720.008600.2953
2820.008600.2953

Node centrality measures in the 14-player network_

Node IDDegree centralityCloseness centralityBetweenness centralityEigenvector centrality
140.027812.50.6244
240.027820.7589
310.02500.0472
4 6 0.035718 1
530.026300.6335
630.032370.5674
740.03237 0.7605
84 0.0385 42.50.6325
930.0357 44 0.1876
1040.029430.50.0664
1120.022200.0239
1230.02270.50.0287
1310.020800.157
1420.022200.0239

Summary of network measures used in the proposed activity_

MeasureDefinition and implicationsKey references
Node-levelDegree centrality•Considers a given node’s number of direct connections•Nodes high in degree centrality have a large number of immediate exchanges of information Borgatti (2005)
Closeness centrality•Considers the average shortest path from a given node to all other nodes in the network•Nodes high in closeness centrality can reach all the other nodes in the network in a short number of steps and, therefore, can be efficient in accessing or sharing information Wasserman and Faust (1994)
Betweenness centrality•Considers the extent to which a given node is positioned between other nodes on their shortest paths, or geodesics•Nodes high in betweenness centrality can serve as a bridge to transport information or control the interactions between other nodes Freeman (1977), Wasserman and Faust (1994)
Eigenvector centrality•Considers the centralities of a given node’s neighbors (in contrast to degree centrality which exclusively relies on the number of connections)•Nodes high in eigenvector centrality are more influential than nodes which have a large number of connections to less central nodes Bonacich (2007)
Overall network-levelDiameter•Measures the distance between the two nodes furthest apart in the network, or the largest geodesic distance across the entire network•Represents the maximum distance a piece of information needs to travel in a network Yamaguchi (1994)
Mean geodesic distance•Measures the average number of shortest steps between pairs of nodes•Reflects the overall connectivity of a network and impacts the extent to which information can be shared among nodes in few steps Hanneman and Riddle (2005)
Clique•A cohesive subgroup of nodes that are all directly connected to all others in the group•Members in a clique have constraints in accessing non-redundant information if they do not have ties to nodes outside of the clique Haythornthwaite (1996), Hanneman and Riddle (2005)
Community structure•Structures of densely connected subsets of nodes•Represents social groupings, impacting the flow of information within and across those boundaries Girvan and Newman (2002)
DOI: https://doi.org/10.21307/connections-2019-005 | Journal eISSN: 2816-4245 | Journal ISSN: 0226-1766
Language: English
Page range: 1 - 18
Published on: Oct 23, 2019
Published by: International Network for Social Network Analysis (INSNA)
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Seungyoon Lee, Zachary Wittrock, Bailey C. Benedict, published by International Network for Social Network Analysis (INSNA)
This work is licensed under the Creative Commons Attribution 4.0 License.