Representing Relational Data as a Graph

Let's say you have entities stored in a relational database, and you wish to view these entities as a graph.

First, you do a data extract of the relevant data:

select e.entity as entity,sum(1) as count,'January' as month, 2019 as year
from entity_select es
join entity e on e.id=es.entity_id
where es.selected_dttm > '2019-01-01' and es.selected_dttm < '2019-02-01'
group by e.entity
order by count desc
limit 20;

Save these data out as CSV (may sure it has headers), 

The resulting saved file looks something like this, doesn't it:

entity,count,month,year

donald trump,1478,January,2019

nancy pelosi,413,January,2019

ralph northam,362,January,2019

department of homeland security,357,January,2019

twitter,312,January,2019

barack obama,246,January,2019

michael cohen,228,January,2019

robert mueller,189,January,2019

mitch mcconnell,178,January,2019

fox news,160,January,2019

jim mattis,155,January,2019

u.s.-mexico border,151,January,2019

jason leopold,147,January,2019

pharrell williams,147,January,2019

islamic state,146,January,2019

kirk cox,142,January,2019

mark warner,132,January,2019

moscow,129,January,2019

house of representatives,128,January,2019

elaine luria,128,January,2019

then import these data into your graph datastore:

LOAD CSV WITH HEADERS FROM "file:///Users/geophf/daters/entities-2019-01.csv" AS row
MERGE (y:Year { year: row.year })
MERGE (m:Month { month: row.month })
MERGE (y)<-[o:Of]-(m)
CREATE (e:Entity { name: row.entity })
CREATE (e)<-[c:Count { count: toInt(row.count) }]-(m)

Query your newly created database with the Cypher query:

MATCH (n) RETURN n LIMIT 25

You get the following result from the above data:

Let's repeat this for months February, March, April, and May, remembering to replace the CREATE keyword with MERGE for entities, now that we may have entities crossing months.

Looking at just the January and February data (and replacing the Count label from its type to its count), we see entities crossing months:

When we bring in March's data, we see an entity shared across the three months: Donald Trump. Surprising, n'est-ce pas?

But between March and April, there are several entities in common:

Okay, now let's tie in May. And, when we do tie in May, we observe an interesting phenomenon: entities 'leapfrogging' over months, as interest in them waxes and wanes.

Question: from the above graph, can you compute which entities are most viewed over the entire span of observation?