Graph DB Cluster identification followed by Cluster Comparison with externally defined Clusters - data-analysis

Question

I have a commercially sensitive system, so I can't describe it directly but will try to use an obfuscated data set and description.
This a question about what analyses are available to run, not how to program it. If you're feeling flamey about 'how' or 'where' I've asked the question, please be a cool human and just point me to the right place to post.
For context, I'm running Gremlin server 3.4.6 in Docker on a Debian VM and Gremlin Console 3.4.4 on a Mac and I'm hoping to import my data into the server from a graphml file that successfully renders in Gephy.
I have
6 'widget types'
30 'Things'
175 'people'
In world A
Each thing is atype of widget
[Each widget type has one or more instance Thing]
Each Thing is used by zero or more people
[Each person uses zero or more things]
In a parallel world, B there's a probably similar structure (we think is similar but we want to find out how similar clusters from each world actually are)
For the graph in world A, we run an analysis (like k-means) primarily to identify clusters of Things and people that work together. Having done that we do something similar to World B
I would then like to know if there's a graph analysis that will give me:
Confidence estimation of affinities between World A groups and World B groups
Snippet of the data (vast majority of data excluded. I have included 1 Type, 1 Thing, 1 Person and a few arbitrary edges):
<?xml version="1.0" encoding="UTF-8"?>
<graphml xmlns="http://graphml.graphdrawing.org/xmlns">
<key attr.name="label" attr.type="string" for="node" id="label"/>
<key attr.name="Edge Label" attr.type="string" for="edge" id="edgelabel"/>
<key attr.name="weight" attr.type="double" for="edge" id="weight"/>
<key attr.name="r" attr.type="int" for="node" id="r"/>
<key attr.name="g" attr.type="int" for="node" id="g"/>
<key attr.name="b" attr.type="int" for="node" id="b"/>
<key attr.name="x" attr.type="float" for="node" id="x"/>
<key attr.name="y" attr.type="float" for="node" id="y"/>
<key attr.name="size" attr.type="float" for="node" id="size"/>
<key attr.name="interval" attr.type="intervalset" for="node" id="interval"/>
<key attr.name="type" attr.type="string" for="node" id="type"/>
<key attr.name="Clustering Coefficient" attr.type="double" for="node" id="clustering"/>
<key attr.name="Number of triangles" attr.type="int" for="node" id="triangles"/>
<key attr.name="Eigenvector Centrality" attr.type="double" for="node" id="eigencentrality"/>
<key attr.name="Inferred Class" attr.type="int" for="node" id="stat_inf_class"/>
<key attr.name="Modularity Class" attr.type="int" for="node" id="modularity_class"/>
<key attr.name="Authority" attr.type="float" for="node" id="authority"/>
<key attr.name="Hub" attr.type="float" for="node" id="hub"/>
<key attr.name="Component ID" attr.type="int" for="node" id="componentnumber"/>
<key attr.name="Cluter-ID" attr.type="int" for="node" id="cluter-id"/>
<key attr.name="Cluster" attr.type="int" for="node" id="cluster"/>
<key attr.name="sourcelabel" attr.type="string" for="edge" id="sourcelabel"/>
<key attr.name="targetlabel" attr.type="string" for="edge" id="targetlabel"/>
<key attr.name="interval" attr.type="intervalset" for="edge" id="interval"/>
<graph edgedefault="undirected">
<node id="1">
<data key="label">WIDGET1</data>
<data key="type">Type/Class</data>
<data key="clustering">0.0</data>
<data key="triangles">0</data>
<data key="eigencentrality">0.0</data>
<data key="stat_inf_class">0</data>
<data key="modularity_class">0</data>
<data key="authority">0.0</data>
<data key="hub">0.0</data>
<data key="componentnumber">0</data>
<data key="cluter-id">0</data>
<data key="cluster">0</data>
<data key="size">10.0</data>
<data key="r">255</data>
<data key="g">26</data>
<data key="b">32</data>
<data key="x">-624.7099</data>
<data key="y">0.96178514</data>
</node>
<node id="7">
<data key="label">ABNDFILE</data>
<data key="type">Thing</data>
<data key="clustering">0.0</data>
<data key="triangles">0</data>
<data key="eigencentrality">0.08110029554883653</data>
<data key="stat_inf_class">12</data>
<data key="modularity_class">10</data>
<data key="authority">0.028190823</data>
<data key="hub">0.0070641674</data>
<data key="componentnumber">6</data>
<data key="cluter-id">19</data>
<data key="cluster">6</data>
<data key="size">10.0</data>
<data key="r">255</data>
<data key="g">26</data>
<data key="b">32</data>
<data key="x">-990.6759</data>
<data key="y">-714.0662</data>
</node>
<node id="46">
<data key="label">CRGN1AAA</data>
<data key="type">person</data>
<data key="clustering">0.0</data>
<data key="triangles">0</data>
<data key="eigencentrality">3.014088737416253E-4</data>
<data key="stat_inf_class">11</data>
<data key="modularity_class">8</data>
<data key="authority">0.0</data>
<data key="hub">0.0</data>
<data key="componentnumber">7</data>
<data key="cluter-id">7</data>
<data key="cluster">45</data>
<data key="size">10.0</data>
<data key="r">255</data>
<data key="g">26</data>
<data key="b">32</data>
<data key="x">828.32367</data>
<data key="y">-881.0575</data>
</node>
<edge id="1" label="JKL" source="46" target="15">
<data key="edgelabel">JKL</data>
<data key="weight">1.0</data>
<data key="sourcelabel">CRGN1AAA</data>
<data key="targetlabel">CWXN</data>
</edge>
<edge id="2" label="JKL" source="47" target="21">
<data key="edgelabel">JKL</data>
<data key="weight">1.0</data>
<data key="sourcelabel">CWEB1AAA</data>
<data key="targetlabel">DFHPISN2</data>
</edge>
<edge id="3" label="JKL" source="47" target="41">
<data key="edgelabel">JKL</data>
<data key="weight">1.0</data>
<data key="sourcelabel">CWEB1AAA</data>
<data key="targetlabel">SWITCHTX</data>
</edge>
</graph>
</graphml>