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

Seungyoon Lee

Seungyoon Lee

Brian Lamb School of Communication, Purdue UniversityWest Lafayette, IN, 47907, United States
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Lee, Seungyoon
, 
Zachary Wittrock

Zachary Wittrock

Lincoln Memorial UniversityDuluth, MN, 55805, United States
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Wittrock, Zachary
 and 
Bailey C. Benedict

Bailey C. Benedict

Brian Lamb School of Communication, Purdue UniversityWest Lafayette, IN, 47907, United States
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Benedict, Bailey C.
  
Oct 23, 2019
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Published Online: Oct 23, 2019
Page range: 1 - 18
DOI: https://doi.org/10.21307/connections-2019-005
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© 2019 Seungyoon Lee et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

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 ID Degree centrality Closeness centrality Betweenness centrality Eigenvector centrality
1 5 0.0106 48.0833 0.7109
2 8 0.011 85.6667 0.9748
3 1 0.0099 0 0.0402
4 6 0.0127 64.8333 1
5 3 0.0101 0 0.6345
6 4 0.0118 46.4167 0.5917
7 4 0.0119 35.3333 0.7543
8 5 0.0137 190.3333 0.6349
9 3 0.0133 191 0.1731
10 6 0.0123 185.5 0.0694
11 4 0.0104 110.5 0.0276
12 4 0.0101 6.5 0.0337
13 2 0.0085 0 0.2267
14 2 0.0094 0 0.024
15 3 0.0086 46 0.0082
16 1 0.0093 0 0.0161
17 2 0.0099 5.25 0.2759
18 2 0.0085 23 0.0075
19 3 0.0085 3.0833 0.2643
20 4 0.0073 27.5 0.0046
21 2 0.0093 5.4167 0.199
22 2 0.0094 0 0.024
23 2 0.0072 0 0.003
24 1 0.0061 0 0.0011
25 2 0.0108 14.5833 0.2118
26 1 0.0085 0 0.2266
27 2 0.0086 0 0.2953
28 2 0.0086 0 0.2953

Node centrality measures in the 14-player network_

Node ID Degree centrality Closeness centrality Betweenness centrality Eigenvector centrality
1 4 0.0278 12.5 0.6244
2 4 0.0278 2 0.7589
3 1 0.025 0 0.0472
4 6 0.0357 18 1
5 3 0.0263 0 0.6335
6 3 0.0323 7 0.5674
7 4 0.0323 7 0.7605
8 4 0.0385 42.5 0.6325
9 3 0.0357 44 0.1876
10 4 0.0294 30.5 0.0664
11 2 0.0222 0 0.0239
12 3 0.0227 0.5 0.0287
13 1 0.0208 0 0.157
14 2 0.0222 0 0.0239

Summary of network measures used in the proposed activity_

Measure Definition and implications Key references
Node-level Degree 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-level Diameter •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)
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Social Sciences, Social Sciences, other
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