Trump Analysis

Results

Figure 1: Network Graphs of All Plaintiff States by AG Political Party. Node size represents degree centrality, and edge widths are weighted.

The network graph displayed in Figure 1 illustrates the plaintiff states, labeled by the political party affiliation of their attorney general at the time the state joined the case.

During the Biden Administration, there were eight states who had a change in the political party of their elected attorney general: Colorado, Guam, Kentucky, Michigan, Mississippi, Nevada, New Jersey, and Wisconsin.

The degree assortativity, or the preference of nodes to attach to nodes of similar degree, is 0.69, revealing assortativity between states with similar number of connections. The political assortativity, preference of states to connect with states of the same political party is 0.76, positively assortative.

The network graph of plaintiff states is presented interactively in the plot, Figure 2.

Figure 2: State Plaintiff Network Graph, plotly visualization. Node and edge size are weighted. Node size represents scaled strength.

Table 1: Top Ten States by Weighted Degree Centrality. Column Centrality Measures include unweighted degree, eigenvector, betweenness, closeness, and strength.

State	Degree	Eigenvector	Betweenness	Closeness	Strength
NY	31	1.00	0.73	0.76	14.03
CA	31	0.88	0.00	0.82	12.29
MA	30	0.71	5.94	0.84	9.96
OR	31	0.66	9.03	0.85	9.26
MD	31	0.66	9.03	0.85	9.21
IL	29	0.63	9.03	0.85	8.88
WA	31	0.62	9.03	0.85	8.64
VT	29	0.60	9.03	0.85	8.36
NJ	30	0.59	5.94	0.84	8.09
MN	31	0.57	9.03	0.85	8.04

Figure 3: Network Graph of Litigators. Node color and shape describes political party affiliation. Node size describes represents degree centrality. Edge size is the number of interactions between any two nodes.

Figure 3 shows the network of judges and attorneys. The edge sizes in the graph are weighted by the number of interactions between each set of two nodes. The graph contains a tight core of Democrat attorneys, surrounded by lesser connected litigators and a smaller cluster of Republican attorneys. This network has a degree assortativity of -0.25, indicating preference for attorneys and judges of differing degree centralities to connect with each other.

An interactive renditions of the litigator network is accessible through Figure 4.

Figure 4: Attorney Network Visualization by plotly.

Table 2: Ten Most Central Judges with Attributes and Network Statistics

Judge Name	Title	Organization	Political Party	Degree	Eigenvector Centrality
Judith Rogers	Circuit Court Judge	D.C. Circuit	Democratic	7.71	0.14
Robert Wilkins	Circuit Court Judge	D.C. Circuit	Democratic	7.24	0.11
Haywood Gilliam	District Court Judge	Northern District of California	Democratic	7.18	0.10
Sidney Thomas	Circuit Court Judge	9th Circuit	Democratic	7.18	0.09
Gregory Katsas	Circuit Court Judge	D.C. Circuit	Republican	6.37	0.09
Mary M Schroeder	Circuit Court Judge	Ninth Circuit Court of Appeals	Democratic	6.37	0.09
Kim Wardlaw	Circuit Court Judge	9th Circuit	Democratic	6.11	0.07
Ketanji Brown Jackson	District Court Judge	District of Columbia District Court	Democratic	5.61	0.09
Karen Henderson	Circuit Court Judge	District of Columbia Circuit Court	Republican	5.03	0.07
Jesse Furman	District Court Judge	Southern District of New York	Democratic	4.68	0.08
a Network Statistics are Weighted.

Table 2 lists the judges in their organization with their respective political party affiliations, degree centrality, and eigenvector centrality. The network includes 161 judges serving across 32 districts.

The judge with the highest degree centrality value is Judith Rogers, a Circut Court Judge on the D.C. Circuit Court of Appeals. The majority of these (7/10) are Circut Court Judges, with most coming from the D.C. Circuit. The Ninth Circuit of Appeals also features prominently on this list. Of the District Court judges in the top ten centrality measures, we see the Northern District of California, the District of Columbia Circuit Court, and the Southern District of New York. Only two of these top ten judges are Republican.

Table 3: Ten Most Central Attorneys with Attributes and Network Statistics

Attorney Name	Title	Organization	Political Party	Strength	Eigenvector Centrality
Maura Healey	Attorney General	Massachusetts Attorney General Office	Democratic	59.20	0.99
Ellen Rosenblum	Attorney General	Oregon Attorney General Office	Democratic	58.36	1.00
Brian Frosh	Attorney General	Maryland Attorney General Office	Democratic	57.24	0.95
Thomas Donovan	Attorney General	Vermont Attorney General Office	Democratic	55.54	0.95
Karl Racine	Attorney General	District of Columbia Attorney General Office	Democratic	51.26	0.85
Xavier Becerra	Attorney General	California Attorney General Office	Democratic	51.05	0.88
Robert Ferguson	Attorney General	Washington Attorney General Office	Democratic	49.25	0.86
Gurbir Grewal	Attorney General	New Jersey Attorney General Office	Democratic	49.05	0.87
Letitia James	Attorney General	New York Attorney General Office	Democratic	46.51	0.83
Joshua Shapiro	Attorney General	Pennsylvania Attoreny General Office	Democratic	44.55	0.75

Table 3 displays the ten most central attorneys by degree centrality. The attorney with the greatest number of weighted connections is Maura Healey, the Massachusetts Attorney General. Ellen Rosenblum, the Attorney General for Oregon, has the highest eigenvector centrality. These top ten attorneys feature soley individuals from the Democrat party. In the list of the top ten attorneys, the only titles that exist are those of the Attorney General.

Figure 5: Degree Centrality for Attorney and Solicitor

After considering the networks of plaintiff states, we show the network of the litigation targets. These are the federal agencies or individuals whom the claims have been filed against. The network represented in Figure 6 depicts the connections among the litigation targets engaged in multistate litigation.

Figure 6: Network of the Litigation Targets

The interaction between plaintiff states and the litigation targets is visualized in Figure 7. The network consists of two distinct types of nodes, defendants and plaintiffs, engaged together in legal cases. This network graph displays the interaction of states arranged by the political party of their attorney general at the time of joining the litigation case with the separate federal agencies and targets.

Figure 7: A Network Graph of the Interactions between States and Federal Agencies. Edges are weighted by the number of connections between nodes.

Next, we address the timing of the number of attorney generals that participate on a litigation case relative to the filing date. Figure 8 shows the relationship of the filing date and the number of attorney generals.

Figure 8: Relationship between Filing Date and Number of Attorney Generals