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= Mission Statement Topics and Admissions Performance =

== Data Model

image::https://dl.dropboxusercontent.com/u/15276022/mision-topics-data-model.png[]

== Intro

Below I use Prismatic's Interest Graph API to tag the mission statements of approximately 500 colleges in the U.S. in order to evaluate the "focus" of each, which I define as the topics extracted from the API.  In addition, I also consider the competitive nature of various schools, commonly refered to as the "Competitor Set".  Lastly, I admissions performance to better understand :

* Determine which schools appear to serve the same mission  
* Are the topics found within mission statements related to admissions performance?

== Collect the data to build the database

At a high level, this is how I built the database:

. Grab the characteristics and admissions performance data for all colleges and universities.  The data are for the year 2013/14 academic year. 
. Filter the data to a subset of approximately 1,000 schools.  
. For each of these schools, crawl the College Navigator profile page of that school in order to grab the URL of the mission statement.  
. I feed the URL to Prismatic's Topic Graph API in order to use their webservice in order to exract the topics of the missions statement. 
. Crawl a popular college search site to extract "similar schools", which we can view as a directed edge between two institutions.  

Some notes:  

* I did nearly all of this work within R.  I will link to the Github repo here. 
* I filtered the universe of colleges based on the following criteria:  
** Schools found within the US (excluding Military institutions)
** Public and not-for-private 4-year degree-granting institutions  
** Enrolled 300 or more first-time first-year students  
* In the end, I was left with about 1,000 schools, but filtered the list even further to ensure that:  
** Only schools that could be tagged by Prismatic's API  
** School's must have competitor data

Obviously I was pretty strict in filtering my data but I analysis is easier when you do not have to worry about missing data.  Because I am just playing around, I am not too worried about throwing away data, although missing data could greatly impact any outcomes if this were more a formal study.

Once all of the data were captured, we load everything in Neo4j in order to leverage the connected nature within the data.

== Setup the database 

Before I move forward, it's worth noting that I created all of my cypher statements by using the dump feature found with Neo4j.

[source]
----
./bin/neo4j-shell -c 'dump MATCH (b:School) -[r1]-> (t:Topic) <-[r2]- (z), (b) -[x:SIMILAR_TO]-> (s:School), (s) -[y:HAS_TOPIC]-> (t2:Topic) WHERE b.unitid = 164739 RETURN b, s, r1, r2, t, z, x, y, t2;' > bentley_data.cypher
----

In the code sample above, I am dumping a subgraph from Neo4j and piping the output to cypher statements.  After removing the begin and commit portions of the dump, I am using those statements in the console below.

//hide
//setup
//output
[source, cypher]
----
create (_1724:`Topic` {`topic`:"Colleges and Universities", `topic_id`:996})
create (_1725:`Topic` {`topic`:"Higher Education", `topic_id`:1929})
create (_1735:`Topic` {`topic`:"Education and Schools", `topic_id`:1360})
create (_1736:`Topic` {`topic`:"International Education", `topic_id`:2115})
create (_1739:`Topic` {`topic`:"Graduate Schools and Students", `topic_id`:1780})
create (_1741:`Topic` {`topic`:"Academic Freedom", `topic_id`:25})
create (_1747:`Topic` {`topic`:"Education", `topic_id`:1359})
create (_1754:`Topic` {`topic`:"Faith", `topic_id`:16149})
create (_1755:`Topic` {`topic`:"Culture", `topic_id`:1113})
create (_1757:`Topic` {`topic`:"Humanities", `topic_id`:2013})
create (_1775:`Topic` {`topic`:"Education Reform", `topic_id`:15129})
create (_1796:`Topic` {`topic`:"Engagement", `topic_id`:15544})
create (_1798:`Topic` {`topic`:"Future", `topic_id`:17313})
create (_1801:`Topic` {`topic`:"University of Chicago", `topic_id`:4605})
create (_1806:`Topic` {`topic`:"Society", `topic_id`:4079})
create (_1814:`Topic` {`topic`:"Political Science", `topic_id`:31430})
create (_1822:`Topic` {`topic`:"Critical Theory", `topic_id`:12740})
create (_1823:`Topic` {`topic`:"Social Justice", `topic_id`:35658})
create (_1827:`Topic` {`topic`:"Scholarships and Fellowships", `topic_id`:3932})
create (_1855:`Topic` {`topic`:"Cultural Studies", `topic_id`:1112})
create (_1858:`Topic` {`topic`:"University of Connecticut", `topic_id`:4607})
create (_1859:`School` {`admit_rate`:0.536592, `admssn`:14745, `ape`:7.317976, `applcn`:27479, `applefeeu`:70, `carnegie`:15, `enrlft`:3755, `instnm`:"University of Connecticut", `iqr`:200, `obereg`:1, `rating`:4.051200, `sat25`:1130, `sat75`:1330, `unitid`:129020})
create (_1860:`Topic` {`topic`:"Connecticut", `topic_id`:1039})
create (_1861:`Topic` {`topic`:"Hartford, Connecticut", `topic_id`:1873})
create (_1864:`School` {`admit_rate`:0.672206, `admssn`:13914, `ape`:11.480311, `applcn`:20699, `applefeeu`:45, `carnegie`:21, `enrlft`:1803, `instnm`:"Quinnipiac University", `iqr`:190, `obereg`:1, `rating`:3.785700, `sat25`:1000, `sat75`:1190, `unitid`:130226})
create (_1865:`Topic` {`topic`:"Quinnipiac University", `topic_id`:3642})
create (_1870:`School` {`admit_rate`:0.431234, `admssn`:7566, `ape`:10.783651, `applcn`:17545, `applefeeu`:70, `carnegie`:15, `enrlft`:1627, `instnm`:"American University", `iqr`:190, `obereg`:2, `rating`:3.745100, `sat25`:1160, `sat75`:1350, `unitid`:131159})
create (_1883:`Topic` {`topic`:"Boston College", `topic_id`:592})
create (_1928:`Topic` {`topic`:"Religion and Belief", `topic_id`:3716})
create (_1975:`Topic` {`topic`:"Morality", `topic_id`:27638})
create (_2040:`School` {`admit_rate`:0.665483, `admssn`:3185, `ape`:13.674286, `applcn`:4786, `applefeeu`:55, `carnegie`:31, `enrlft`:350, `instnm`:"Washington College", `iqr`:240, `obereg`:2, `rating`:4.100000, `sat25`:1040, `sat75`:1280, `unitid`:164216})
create (_2041:`Topic` {`topic`:"Ethics", `topic_id`:1448})
create (_2046:`School` {`admit_rate`:0.437875, `admssn`:3281, `ape`:7.693018, `applcn`:7493, `applefeeu`:50, `carnegie`:21, `enrlft`:974, `instnm`:"Bentley University", `iqr`:170, `obereg`:1, `rating`:3.943800, `sat25`:1140, `sat75`:1310, `unitid`:164739})
create (_2048:`Topic` {`topic`:"Business Ethics", `topic_id`:10218})
create (_2049:`School` {`admit_rate`:0.322153, `admssn`:7905, `ape`:11.078104, `applcn`:24538, `applefeeu`:70, `carnegie`:15, `enrlft`:2215, `instnm`:"Boston College", `iqr`:180, `obereg`:1, `rating`:4.076500, `sat25`:1270, `sat75`:1450, `unitid`:164924})
create (_2050:`School` {`admit_rate`:0.456097, `admssn`:20071, `ape`:11.350529, `applcn`:44006, `applefeeu`:80, `carnegie`:15, `enrlft`:3877, `instnm`:"Boston University", `iqr`:210, `obereg`:1, `rating`:4.211900, `sat25`:1180, `sat75`:1390, `unitid`:164988})
create (_2051:`Topic` {`topic`:"Boston", `topic_id`:589})
create (_2052:`School` {`admit_rate`:0.370366, `admssn`:3517, `ape`:11.399760, `applcn`:9496, `applefeeu`:75, `carnegie`:15, `enrlft`:833, `instnm`:"Brandeis University", `iqr`:240, `obereg`:1, `rating`:3.885100, `sat25`:1230, `sat75`:1470, `unitid`:165015})
create (_2067:`School` {`admit_rate`:0.714076, `admssn`:5834, `ape`:5.782024, `applcn`:8170, `applefeeu`:60, `carnegie`:16, `enrlft`:1413, `instnm`:"University of Massachusetts-Boston", `iqr`:230, `obereg`:1, `rating`:3.184400, `sat25`:900, `sat75`:1130, `unitid`:166638})
create (_2068:`Topic` {`topic`:"University of Massachusetts", `topic_id`:4621})
create (_2079:`School` {`admit_rate`:0.825013, `admssn`:7652, `ape`:7.807239, `applcn`:9275, `applefeeu`:50, `carnegie`:21, `enrlft`:1188, `instnm`:"Suffolk University", `iqr`:240, `obereg`:1, `rating`:3.631600, `sat25`:890, `sat75`:1130, `unitid`:168005})
create (_2096:`School` {`admit_rate`:0.653766, `admssn`:946, `ape`:4.759868, `applcn`:1447, `applefeeu`:25, `carnegie`:55, `enrlft`:304, `instnm`:"Northwood University-Michigan", `iqr`:290, `obereg`:3, `rating`:3.725500, `sat25`:860, `sat75`:1150, `unitid`:171492})
create (_2110:`School` {`admit_rate`:0.726519, `admssn`:1315, `ape`:4.189815, `applcn`:1810, `applefeeu`:0, `carnegie`:21, `enrlft`:432, `instnm`:"The College of Saint Scholastica", `iqr`:180, `obereg`:4, `rating`:3.843800, `sat25`:850, `sat75`:1030, `unitid`:174899})
create (_2120:`School` {`admit_rate`:0.663072, `admssn`:4058, `ape`:3.505155, `applcn`:6120, `applefeeu`:35, `carnegie`:15, `enrlft`:1746, `instnm`:"University of Southern Mississippi", `iqr`:200, `obereg`:5, `rating`:4.061700, `sat25`:910, `sat75`:1110, `unitid`:176372})
create (_2121:`Topic` {`topic`:"Innovation", `topic_id`:2086})
create (_2154:`Topic` {`topic`:"New Hampshire", `topic_id`:3088})
create (_2155:`School` {`admit_rate`:0.778403, `admssn`:13963, `ape`:6.252353, `applcn`:17938, `applefeeu`:65, `carnegie`:15, `enrlft`:2869, `instnm`:"University of New Hampshire-Main Campus", `iqr`:200, `obereg`:1, `rating`:4.163300, `sat25`:1000, `sat75`:1200, `unitid`:183044})
create (_2156:`Topic` {`topic`:"University of New Hampshire", `topic_id`:4629})
create (_2240:`School` {`admit_rate`:0.494888, `admssn`:13990, `ape`:8.106969, `applcn`:28269, `applefeeu`:75, `carnegie`:15, `enrlft`:3487, `instnm`:"Syracuse University", `iqr`:230, `obereg`:2, `rating`:3.984500, `sat25`:1040, `sat75`:1270, `unitid`:196413})
create (_2257:`School` {`admit_rate`:0.680596, `admssn`:3241, `ape`:15.361290, `applcn`:4762, `applefeeu`:35, `carnegie`:32, `enrlft`:310, `instnm`:"Lenoir-Rhyne University", `iqr`:200, `obereg`:5, `rating`:3.796600, `sat25`:880, `sat75`:1080, `unitid`:198835})
create (_2279:`School` {`admit_rate`:0.768599, `admssn`:1498, `ape`:6.167722, `applcn`:1949, `applefeeu`:0, `carnegie`:22, `enrlft`:316, `instnm`:"Queens University of Charlotte", `iqr`:200, `obereg`:5, `rating`:3.838700, `sat25`:940, `sat75`:1140, `unitid`:199412})
create (_2287:`School` {`admit_rate`:0.841019, `admssn`:4888, `ape`:2.276537, `applcn`:5812, `applefeeu`:35, `carnegie`:16, `enrlft`:2553, `instnm`:"North Dakota State University-Main Campus", `iqr`:295, `obereg`:4, `rating`:3.914800, `sat25`:980, `sat75`:1275, `unitid`:200332})
create (_2302:`School` {`admit_rate`:0.875522, `admssn`:1048, `ape`:3.694444, `applcn`:1197, `applefeeu`:25, `carnegie`:22, `enrlft`:324, `instnm`:"College of Mount St Joseph", `iqr`:290, `obereg`:3, `rating`:4.121200, `sat25`:780, `sat75`:1070, `unitid`:204200})
create (_2339:`School` {`admit_rate`:0.234868, `admssn`:842, `ape`:10.863636, `applcn`:3585, `applefeeu`:60, `carnegie`:31, `enrlft`:330, `instnm`:"Haverford College", `iqr`:180, `obereg`:2, `rating`:4.087000, `sat25`:1310, `sat75`:1490, `unitid`:212911})
create (_2352:`Topic` {`topic`:"Pennsylvania", `topic_id`:3423})
create (_2354:`Topic` {`topic`:"Pittsburgh, Pennsylvania", `topic_id`:3499})
create (_2355:`Topic` {`topic`:"University of Pennsylvania", `topic_id`:4635})
create (_2364:`School` {`admit_rate`:0.122435, `admssn`:3830, `ape`:12.921107, `applcn`:31282, `applefeeu`:75, `carnegie`:15, `enrlft`:2421, `instnm`:"University of Pennsylvania", `iqr`:180, `obereg`:2, `rating`:3.930200, `sat25`:1360, `sat75`:1540, `unitid`:215062})
create (_2369:`School` {`admit_rate`:0.489042, `admssn`:7319, `ape`:9.021097, `applcn`:14966, `applefeeu`:80, `carnegie`:21, `enrlft`:1659, `instnm`:"Villanova University", `iqr`:190, `obereg`:2, `rating`:4.102700, `sat25`:1210, `sat75`:1400, `unitid`:216597})
create (_2372:`School` {`admit_rate`:0.765508, `admssn`:4603, `ape`:6.756180, `applcn`:6013, `applefeeu`:50, `carnegie`:21, `enrlft`:890, `instnm`:"Bryant University", `iqr`:170, `obereg`:1, `rating`:3.913000, `sat25`:1050, `sat75`:1220, `unitid`:217165})
create (_2394:`School` {`admit_rate`:0.864859, `admssn`:4083, `ape`:2.509835, `applcn`:4721, `applefeeu`:25, `carnegie`:16, `enrlft`:1881, `instnm`:"East Tennessee State University", `iqr`:210, `obereg`:5, `rating`:3.749000, `sat25`:890, `sat75`:1100, `unitid`:220075})
create (_2403:`School` {`admit_rate`:0.724854, `admssn`:10435, `ape`:3.366698, `applcn`:14396, `applefeeu`:40, `carnegie`:15, `enrlft`:4276, `instnm`:"The University of Tennessee-Knoxville", `iqr`:250, `obereg`:5, `rating`:3.911300, `sat25`:1040, `sat75`:1290, `unitid`:221759})
create (_2498:`School` {`admit_rate`:0.552303, `admssn`:16679, `ape`:4.829522, `applcn`:30199, `applefeeu`:60, `carnegie`:15, `enrlft`:6253, `instnm`:"University of Washington-Seattle Campus", `iqr`:260, `obereg`:8, `rating`:3.914400, `sat25`:1100, `sat75`:1360, `unitid`:236948})
create _1859-[:`HAS_TOPIC` {`score`:0.516330}]->_1861
create _1859-[:`HAS_TOPIC` {`score`:0.519520}]->_1725
create _1859-[:`HAS_TOPIC` {`score`:0.545120}]->_1736
create _1859-[:`HAS_TOPIC` {`score`:0.551270}]->_1741
create _1859-[:`HAS_TOPIC` {`score`:0.562050}]->_1858
create _1859-[:`HAS_TOPIC` {`score`:0.592840}]->_1860
create _1859-[:`HAS_TOPIC` {`score`:0.645370}]->_1724
create _1859-[:`SIMILAR_TO` {`rank`:12}]->_2182
create _1859-[:`SIMILAR_TO` {`rank`:6}]->_1864
create _1859-[:`SIMILAR_TO` {`rank`:25}]->_2270
create _1859-[:`SIMILAR_TO` {`rank`:3}]->_2375
create _1859-[:`SIMILAR_TO` {`rank`:16}]->_2031
create _1859-[:`SIMILAR_TO` {`rank`:17}]->_2369
create _1859-[:`SIMILAR_TO` {`rank`:4}]->_2050
create _1859-[:`SIMILAR_TO` {`rank`:14}]->_2155
create _1859-[:`SIMILAR_TO` {`rank`:18}]->_1863
create _1859-[:`SIMILAR_TO` {`rank`:7}]->_2361
create _1859-[:`SIMILAR_TO` {`rank`:5}]->_2240
create _1859-[:`SIMILAR_TO` {`rank`:10}]->_2049
create _1859-[:`SIMILAR_TO` {`rank`:23}]->_2195
create _1859-[:`SIMILAR_TO` {`rank`:11}]->_1857
create _1859-[:`SIMILAR_TO` {`rank`:15}]->_1866
create _1864-[:`HAS_TOPIC` {`score`:0.511520}]->_1735
create _1864-[:`HAS_TOPIC` {`score`:0.513200}]->_1747
create _1864-[:`HAS_TOPIC` {`score`:0.516860}]->_1736
create _1864-[:`HAS_TOPIC` {`score`:0.519190}]->_1739
create _1864-[:`HAS_TOPIC` {`score`:0.524870}]->_1725
create _1864-[:`HAS_TOPIC` {`score`:0.529310}]->_1865
create _1864-[:`HAS_TOPIC` {`score`:0.608740}]->_1724
create _1864-[:`SIMILAR_TO` {`rank`:16}]->_2240
create _1864-[:`SIMILAR_TO` {`rank`:10}]->_2155
create _1864-[:`SIMILAR_TO` {`rank`:13}]->_1863
create _1864-[:`SIMILAR_TO` {`rank`:23}]->_1866
create _1864-[:`SIMILAR_TO` {`rank`:1}]->_1859
create _1864-[:`SIMILAR_TO` {`rank`:9}]->_2211
create _1864-[:`SIMILAR_TO` {`rank`:6}]->_2375
create _1864-[:`SIMILAR_TO` {`rank`:11}]->_2050
create _1870-[:`HAS_TOPIC` {`score`:0.528890}]->_1814
create _1870-[:`HAS_TOPIC` {`score`:0.532460}]->_1775
create _1870-[:`HAS_TOPIC` {`score`:0.541520}]->_1823
create _1870-[:`HAS_TOPIC` {`score`:0.577710}]->_1741
create _1870-[:`HAS_TOPIC` {`score`:0.595250}]->_1725
create _1870-[:`HAS_TOPIC` {`score`:0.629620}]->_1724
create _1870-[:`HAS_TOPIC` {`score`:0.659100}]->_1736
create _1870-[:`SIMILAR_TO` {`rank`:21}]->_2361
create _1870-[:`SIMILAR_TO` {`rank`:13}]->_2080
create _1870-[:`SIMILAR_TO` {`rank`:20}]->_2064
create _1870-[:`SIMILAR_TO` {`rank`:12}]->_2364
create _1870-[:`SIMILAR_TO` {`rank`:2}]->_1873
create _1870-[:`SIMILAR_TO` {`rank`:6}]->_2240
create _1870-[:`SIMILAR_TO` {`rank`:15}]->_2467
create _1870-[:`SIMILAR_TO` {`rank`:24}]->_2369
create _1870-[:`SIMILAR_TO` {`rank`:25}]->_2248
create _1870-[:`SIMILAR_TO` {`rank`:17}]->_2195
create _1870-[:`SIMILAR_TO` {`rank`:3}]->_2050
create _1870-[:`SIMILAR_TO` {`rank`:8}]->_2031
create _1870-[:`SIMILAR_TO` {`rank`:14}]->_2485
create _1870-[:`SIMILAR_TO` {`rank`:10}]->_2049
create _2040-[:`HAS_TOPIC` {`score`:0.502130}]->_2042
create _2040-[:`HAS_TOPIC` {`score`:0.508950}]->_2041
create _2040-[:`HAS_TOPIC` {`score`:0.523220}]->_1984
create _2040-[:`HAS_TOPIC` {`score`:0.548870}]->_1949
create _2040-[:`SIMILAR_TO` {`rank`:3}]->_2039
create _2040-[:`SIMILAR_TO` {`rank`:12}]->_1870
create _2040-[:`SIMILAR_TO` {`rank`:10}]->_2338
create _2040-[:`SIMILAR_TO` {`rank`:14}]->_2036
create _2040-[:`SIMILAR_TO` {`rank`:4}]->_2031
create _2040-[:`SIMILAR_TO` {`rank`:20}]->_2467
create _2040-[:`SIMILAR_TO` {`rank`:25}]->_2050
create _2040-[:`SIMILAR_TO` {`rank`:1}]->_2038
create _2040-[:`SIMILAR_TO` {`rank`:23}]->_1873
create _2040-[:`SIMILAR_TO` {`rank`:21}]->_2361
create _2046-[:`HAS_TOPIC` {`score`:0.519460}]->_2048
create _2046-[:`HAS_TOPIC` {`score`:0.537760}]->_2047
create _2046-[:`HAS_TOPIC` {`score`:0.552680}]->_2041
create _2046-[:`SIMILAR_TO` {`rank`:5}]->_2049
create _2046-[:`SIMILAR_TO` {`rank`:24}]->_2364
create _2046-[:`SIMILAR_TO` {`rank`:18}]->_2052
create _2046-[:`SIMILAR_TO` {`rank`:10}]->_2369
create _2046-[:`SIMILAR_TO` {`rank`:6}]->_2050
create _2046-[:`SIMILAR_TO` {`rank`:2}]->_2372
create _2046-[:`SIMILAR_TO` {`rank`:16}]->_2155
create _2046-[:`SIMILAR_TO` {`rank`:12}]->_2240
create _2046-[:`SIMILAR_TO` {`rank`:22}]->_2067
create _2046-[:`SIMILAR_TO` {`rank`:21}]->_1870
create _2046-[:`SIMILAR_TO` {`rank`:7}]->_1859
create _2046-[:`SIMILAR_TO` {`rank`:9}]->_2079
create _2046-[:`SIMILAR_TO` {`rank`:15}]->_1864
create _2049-[:`HAS_TOPIC` {`score`:0.682790}]->_1883
create _2049-[:`HAS_TOPIC` {`score`:0.576660}]->_1741
create _2049-[:`HAS_TOPIC` {`score`:0.558240}]->_1724
create _2049-[:`HAS_TOPIC` {`score`:0.525820}]->_1928
create _2049-[:`HAS_TOPIC` {`score`:0.510890}]->_1755
create _2049-[:`HAS_TOPIC` {`score`:0.509100}]->_1806
create _2049-[:`HAS_TOPIC` {`score`:0.501270}]->_1757
create _2049-[:`SIMILAR_TO` {`rank`:3}]->_1873
create _2049-[:`SIMILAR_TO` {`rank`:9}]->_2369
create _2049-[:`SIMILAR_TO` {`rank`:18}]->_2485
create _2049-[:`SIMILAR_TO` {`rank`:12}]->_2064
create _2049-[:`SIMILAR_TO` {`rank`:23}]->_2175
create _2049-[:`SIMILAR_TO` {`rank`:1}]->_2050
create _2049-[:`SIMILAR_TO` {`rank`:16}]->_1971
create _2049-[:`SIMILAR_TO` {`rank`:13}]->_2151
create _2049-[:`SIMILAR_TO` {`rank`:22}]->_2270
create _2049-[:`SIMILAR_TO` {`rank`:7}]->_2195
create _2049-[:`SIMILAR_TO` {`rank`:10}]->_2248
create _2049-[:`SIMILAR_TO` {`rank`:8}]->_2364
create _2049-[:`SIMILAR_TO` {`rank`:6}]->_2080
create _2050-[:`HAS_TOPIC` {`score`:0.572940}]->_1736
create _2050-[:`HAS_TOPIC` {`score`:0.552310}]->_1724
create _2050-[:`HAS_TOPIC` {`score`:0.545360}]->_2051
create _2050-[:`HAS_TOPIC` {`score`:0.541970}]->_1827
create _2050-[:`HAS_TOPIC` {`score`:0.508100}]->_1741
create _2050-[:`SIMILAR_TO` {`rank`:7}]->_2364
create _2050-[:`SIMILAR_TO` {`rank`:25}]->_2335
create _2050-[:`SIMILAR_TO` {`rank`:14}]->_2080
create _2050-[:`SIMILAR_TO` {`rank`:3}]->_2049
create _2050-[:`SIMILAR_TO` {`rank`:11}]->_1873
create _2050-[:`SIMILAR_TO` {`rank`:16}]->_1805
create _2050-[:`SIMILAR_TO` {`rank`:6}]->_2195
create _2050-[:`SIMILAR_TO` {`rank`:18}]->_2248
create _2050-[:`SIMILAR_TO` {`rank`:8}]->_2240
create _2050-[:`SIMILAR_TO` {`rank`:23}]->_2361
create _2050-[:`SIMILAR_TO` {`rank`:22}]->_1870
create _2050-[:`SIMILAR_TO` {`rank`:24}]->_2175
create _2050-[:`SIMILAR_TO` {`rank`:9}]->_2064
create _2052-[:`HAS_TOPIC` {`score`:0.554360}]->_1736
create _2052-[:`HAS_TOPIC` {`score`:0.559270}]->_1822
create _2052-[:`HAS_TOPIC` {`score`:0.583620}]->_1855
create _2052-[:`HAS_TOPIC` {`score`:0.596110}]->_1757
create _2052-[:`HAS_TOPIC` {`score`:0.602940}]->_1741
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create _2403-[:`SIMILAR_TO` {`rank`:14}]->_2115
create _2403-[:`SIMILAR_TO` {`rank`:21}]->_2391
create _2403-[:`SIMILAR_TO` {`rank`:2}]->_2398
create _2403-[:`SIMILAR_TO` {`rank`:16}]->_2384
create _2403-[:`SIMILAR_TO` {`rank`:6}]->_2394
create _2403-[:`SIMILAR_TO` {`rank`:7}]->_1733
create _2498-[:`HAS_TOPIC` {`score`:0.504860}]->_2489
create _2498-[:`HAS_TOPIC` {`score`:0.508540}]->_2041
create _2498-[:`HAS_TOPIC` {`score`:0.525400}]->_1725
create _2498-[:`HAS_TOPIC` {`score`:0.584460}]->_2496
create _2498-[:`HAS_TOPIC` {`score`:0.597700}]->_1731
create _2498-[:`SIMILAR_TO` {`rank`:22}]->_1841
create _2498-[:`SIMILAR_TO` {`rank`:2}]->_2499
create _2498-[:`SIMILAR_TO` {`rank`:19}]->_2518
create _2498-[:`SIMILAR_TO` {`rank`:20}]->_2490
create _2498-[:`SIMILAR_TO` {`rank`:23}]->_1812
create _2498-[:`SIMILAR_TO` {`rank`:1}]->_2495
create _2498-[:`SIMILAR_TO` {`rank`:16}]->_1809
create _2498-[:`SIMILAR_TO` {`rank`:5}]->_1805
create _2498-[:`SIMILAR_TO` {`rank`:12}]->_2492
create _2498-[:`SIMILAR_TO` {`rank`:14}]->_2050
create _2498-[:`SIMILAR_TO` {`rank`:25}]->_2195
create _2498-[:`SIMILAR_TO` {`rank`:13}]->_2488
create _2498-[:`SIMILAR_TO` {`rank`:18}]->_1804
----

Let's take a quick peak at what we have:

==== Plot the subgraph

//graph

==== count of distinct node type

[source, cypher]
----
MATCH n 
RETURN DISTINCT LABELS(n), COUNT(n)
----

// table