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view/download model file: lawprofdiffusion.nlogo
We rely upon the default Barabasi and Albert preferential attachment model contained in Netlogo Models library. Additionally, we rely upon the Adamic and Eytan's modification of the default Barabasi & Albert model.
Michael Bommarito from the University of Michigan-Center for Complex Systems wrote a Python script which imported into Netlogo our (.net) edge list representing the hiring and placement of American Law Professoriate.
Each of the 184 Nodes contained herein represents a given Law School listed in the US News and World Report.
This is a diffusion model where we consider the spread of particular paradigms across our empirically derived network. The model represents a fairly simple conceptualization of a diffusion process. For this initial offering, we favor parsimony over a heavily parameterized approach. Despite the limitations of a simple model, it provides insight into the manner that social position within the network of the American Legal Academy impacts the prospects for diffusion.
After laying out the network (see instructions below) and setting the NUM-NODES slider at exactly 185, the model will infect a single institution of your choosing. It is possible to step through the diffusion process through either step by step SPREAD-ONCE or until every node is infected SPREAD. The plot documents the number of nodes infected at each time point. The time monitor will give the current time, and in the case of the 'spread complete' option, how many steps it took until all the nodes are infected. Note that this is the SI model - nodes are either susceptible or infected and there is no recovery. The "p" parameter gives the probability that an infected node will infect a neighbor at each time step. As we do not model host susceptibility, this probability is fixed at each time step.
(1) Make Sure to set NUM-NODES Slider to 185.
(2) Select a value for the "p" parameter.
(3) Select the SETUP Button. This provides a circular layout of the graph.
(4) (Optional) Select the LAYOUT Button. This transforms the circular graph using the Fruchterman-Reingold spring embedded, force directed placement algorithm.
(5) Use the INFECTED Slider to select the Specific Institution {1-185} (see list below) from which to release an idea.
(6) The SPREAD and SPREAD-ONCE Buttons offer alternative methods to run the diffusion process. The SPREAD button continues the diffusion until every node is reached. The SPREAD-ONCE ticks the model forward step by step.
(7) (Optional) TOGGLE INFECTION TREE provides a view of the infected nodes and discrete paths on the network traversed by the infection.
(8) REINFECT-ONE allows the user to reset the simulation. The user may set the INFECTED Slider to the same or a new institution.
The Diffusion Curve for a given institution is plotted in the Number Infected Box as a function of time and n of institutions reached by a given idea.
1 "Yale"
2 "Harvard"
3 "Stanford"
4 "NYU"
5 "Columbia"
6 "Chicago"
7 "Penn"
8 "Berkeley"
9 "Michigan"
10 "Duke"
11 "Virginia"
12 "Northwestern"
13 "Cornell"
14 "Georgetown"
15 "UCLA"
16 "USC"
17 "Vanderbilt"
18 "Texas"
19 "Wash Univ"
20 "Boston Univ"
21 "Minnesota"
22 "Emory"
23 "George Washington"
24 "Iowa"
25 "Fordham"
26 "Illinois"
27 "Washington & Lee"
28 "Boston College"
29 "Notre Dame"
30 "Washington"
31 "William & Mary"
32 "Ohio State"
33 "Wisconsin"
34 "George Mason"
35 "UC Davis"
36 "Indiana"
37 "Alabama"
38 "UC Hastings"
39 "Colorado"
40 "Georgia"
41 "Maryland"
42 "UNC"
43 "Wake Forest"
44 "BYU"
45 "Arizona"
46 "SMU"
47 "American"
48 "Tulane"
49 "Connecticut"
50 "Florida"
51 "Arizona State"
52 "Cardozo Yeshiva"
53 "Baylor"
54 "Case Western"
55 "Florida State"
56 "Tennessee"
57 "Cincinnati"
58 "Pittsburgh"
59 "Utah"
60 "Brooklyn"
61 "Chicago Kent"
62 "Temple"
63 "Houston"
64 "Kentucky"
65 "Villanova"
66 "Loyola LA"
67 "Pepperdine"
68 "Kansas"
69 "Missouri"
70 "Loyola Chicago"
71 "Rutgers Camden"
72 "Seton Hall"
73 "St John's"
74 "Miami"
75 "New Mexico"
76 "Oklahoma"
77 "Rutgers Newark"
78 "SUNY Buffalo"
79 "Denver"
80 "Nebraska"
81 "Richmond"
82 "Georgia State"
83 "Lewis&Clark"
84 "Oregon"
85 "Indiana-Indianapolis"
86 "Northeastern"
87 "Seattle"
88 "St Louis"
89 "San Diego"
90 "Toledo"
91 "DePaul"
92 "Louisiana State"
93 "Penn State"
94 "Santa Clara"
95 "Hawaii"
96 "South Carolina"
97 "Catholic"
98 "Marquette"
99 "Louisville"
100 "Mercer"
101 "Stetson"
102 "UNLV"
103 "San Francisco"
104 "Pacific"
105 "Albany"
106 "Cleveland Marshall"
107 "Creighton"
108 "Drake"
109 "Florida Intl"
110 "Franklin Pierce"
111 "Gonzaga"
112 "Hofstra"
113 "Howard"
114 "Loyola NOLA"
115 "Michigan State"
116 "New York Law School"
117 "Pace"
118 "Quinnipiac"
119 "Samford"
120 "Southern Illinois"
121 "Southwestern"
122 "Suffolk"
123 "Syracuse"
124 "Texas Tech"
125 "Akron"
126 "Arkansas Fayetteville"
127 "Arkansas Little Rock"
128 "Idaho"
129 "Maine"
130 "Memphis"
131 "Mississippi"
132 "Missouri KC"
133 "Montana"
134 "St Thomas MN"
135 "Wyoming"
136 "Vermont"
137 "Washburn"
138 "West Virginia"
139 "William Mitchell"
140 "Appalachian"
141 "Ave Maria"
142 "Barry Univ"
143 "CUNY Queens"
144 "California Western"
145 "Campbell"
146 "Capital"
147 "Chapman"
148 "Duquesne"
149 "Florida Coastal"
150 "Golden Gate"
151 "Hamline"
152 "John Marshall"
153 "Mississippi College"
154 "New England"
155 "North Carolina Central"
156 "Northern Illinois"
157 "Northern Kentucky"
158 "Nova Southeastern"
159 "Ohio Northern"
160 "Oklahoma City"
161 "Regent"
162 "Roger Williams"
163 "South Texas"
164 "Southern"
165 "St Mary's"
166 "St Thomas FL"
167 "Texas Southern"
168 "Texas Wesleyan"
169 "Thomas Jefferson"
170 "Thomas Cooley"
171 "Touro"
172 "Baltimore"
173 "Dayton"
174 "Detroit Mercy"
175 "North Dakota"
176 "South Dakota"
177 "Tulsa"
178 " Univ of DC"
179 "Valparaiso"
180 "Wayne State"
181 "Western New England"
182 "Whittier"
183 "Widener"
184 "Willamette"
(a) Differential Host Susceptibility
(b) Countervailing Paradigms
(c) S I R Model Susceptible-Infected-Recovered
(d) Differential Recovery Times
(e) Density Dependence
This model is based on:
Albert-Laszlo Barabasi. Linked: The New Science of Networks, Perseus Publishing, Cambridge, Massachusetts, pages 79-92.
For a more technical treatment, see:
Albert-Laszlo Barabasi & Reka Albert. Emergence of Scaling in Random Networks, Science, Vol 286, Issue 5439, 15 October 1999, pages 509-512.
The layout algorithm is based on the Fruchterman-Reingold layout algorithm. More information about this algorithm can be obtained at: http://citeseer.ist.psu.edu/fruchterman91graph.html.
We thank also acknowledge Adamic and Eytan for providing the plurality of the code contained herein.
For a model similar to the one described in the first extension, please consult:
W. Brian Arthur, "Urban Systems and Historical Path-Dependence", Chap. 4 in Urban systems and Infrastructure, J. Ausubel and R. Herman (eds.), National Academy of Sciences, Washington, D.C., 1988.
To refer to this model in academic publications, please use: Wilensky, U. (2005). NetLogo Preferential Attachment model.
http://ccl.northwestern.edu/netlogo/models/PreferentialAttachment. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
In other publications, please use: Copyright 2005 Uri Wilensky. All rights reserved. See http://ccl.northwestern.edu/netlogo/models/PreferentialAttachment for terms of use.