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Importing and querying the web of Belgian Public companies and their ceo's/chairmen
//Importing from the Google Spreadsheet
//import the Person nodes
load csv with headers from
"https://docs.google.com/spreadsheets/d/1_X628w_2Lx8ZAIPQQUAGhoDTuf31MRxY821E5D3u2Nc/export?format=csv&id=1_X628w_2Lx8ZAIPQQUAGhoDTuf31MRxY821E5D3u2Nc&gid=0" as persons
create (n:Node:Person)
set n = persons;
//import the Company nodes
load csv with headers from
"https://docs.google.com/spreadsheets/d/1_X628w_2Lx8ZAIPQQUAGhoDTuf31MRxY821E5D3u2Nc/export?format=csv&id=1_X628w_2Lx8ZAIPQQUAGhoDTuf31MRxY821E5D3u2Nc&gid=2040965723" as companies
create (n:Node:Company)
set n = companies;
/////////////////////////////////////////////
//IMPORTING THE RELATIONSHIPS: 2 ALTERNATIVES
/////////////////////////////////////////////
//IF YOU WANT GENERIC RELATIONSHIP TYPE AND JUST WANT TO USE STANDARD CYPHER, USE THIS
//import the relationships
load csv with headers from "https://docs.google.com/spreadsheets/d/1_X628w_2Lx8ZAIPQQUAGhoDTuf31MRxY821E5D3u2Nc/export?format=csv&id=1_X628w_2Lx8ZAIPQQUAGhoDTuf31MRxY821E5D3u2Nc&gid=773066509" as csv
match (source:Node {ID: csv.source}), (target:Node {ID: csv.target})
create (source)-[:RELATED_TO {type: csv.mandate}]->(target);
//IF YOU WANT SPECIFIC RELATIONSHIP TYPES AND HAVE APOCs ENABLED, USE THIS
//specific rels
load csv with headers from "https://docs.google.com/spreadsheets/d/1_X628w_2Lx8ZAIPQQUAGhoDTuf31MRxY821E5D3u2Nc/export?format=csv&id=1_X628w_2Lx8ZAIPQQUAGhoDTuf31MRxY821E5D3u2Nc&gid=773066509" as csv
match (source:Node {ID: csv.source}), (target:Node {ID: csv.target})
CALL apoc.create.relationship(source,csv.mandate,{},target) yield rel
return count(*);
//Colour males and females with new labels
match (n:Person)
where n.gender = "M"
set n:Male;
match (n:Person)
where n.gender = "V"
set n:Female;
//remove the old labels
match (n:Node)
remove n:Node;
//create the INDEXES
create index on :Male(name);
create index on :Female(name);
create index on :Company(name);
//import from original files
//import the Person nodes
load csv with headers from
"http://multimedia.tijd.be/bestuurders/data/nodesdef.csv" as nodes
create (n:Node)
set n = nodes;
//import the relationships
load csv with headers from "http://multimedia.tijd.be/bestuurders/data/linksdef.csv" as rels
match (source:Node {ID: rels.source}), (target:Node {ID: rels.target})
create (source)-[:RELATED_TO {type: rels.mandate}]->(target);
//Queries on the Belgian Public Company Dataset
// degree of the Persons
match (p:Person)
return p.name, size( (p)--() ) as degree
order by degree desc
limit 10
//3-hop network around Luc Bertrand
match path = (m:Male)-[r*..3]-(n)
where m.name contains "Bertrand"
return path
//explore the links between highly connected nodes
//between Philippe Vlerick and Luc Bertrand
match (vlerick:Person {name:"Philippe Vlerick"}), (bertrand:Person {name:"Luc Bertrand"}),
path = allshortestpaths ((vlerick)-[*]-(bertrand))
return path;
//Between Bert De Graeve and Luc Bertrand
match (degraeve:Person {name:"Bert De Graeve"}), (bertrand:Person {name:"Luc Bertrand"}),
path = allshortestpaths ((degraeve)-[*]-(bertrand))
return path;
//Between Bert De Graeve and Frank Donck
match (degraeve:Person {name:"Bert De Graeve"}), (donck:Person {name:"Frank Donck"}),
path = allshortestpaths ((degraeve)-[*]-(donck))
return path;
//explore some links between companies
match (kbc:Company {name:"KBC"}), (li:Company {name:"AB INBEV"}),
path = allshortestpaths ((kbc)-[*]-(li))
return path;
//what is the maximum diameter of the graph
//maximum diameter as text
MATCH (a:Person), (b:Person) WHERE id(a) > id(b)
MATCH p=shortestPath((a)-[:RELATED_TO*]-(b))
with length(p) AS len, extract(x IN nodes(p) | x.name) AS path
ORDER BY len DESC LIMIT 1
return path
//maximum diameter as a graph
MATCH (a:Person), (b:Person) WHERE id(a) > id(b)
MATCH p=shortestPath((a)-[:RELATED_TO*]-(b))
with length(p) AS len, p
ORDER BY len DESC LIMIT 1
return p
//explore some graph algos with APOC
//algos
//betweenness centrality
MATCH (node:Person)
WHERE id(node) %2 = 0
WITH collect(node) AS nodes
CALL apoc.algo.betweenness(['RELATED_TO'],nodes,'BOTH') YIELD node, score
RETURN node.name, score
ORDER BY score DESC
//closeness centrality
MATCH (node:Person)
WHERE id(node) %2 = 0
WITH collect(node) AS nodes
CALL apoc.algo.closeness(['RELATED_TO'],nodes,'INCOMING') YIELD node, score
RETURN node.name, score
ORDER BY score DESC
//pageRank for Companies
MATCH (node:Company)
WHERE id(node) %2 = 0
WITH collect(node) AS nodes
// compute over relationships of all types
CALL apoc.algo.pageRank(nodes) YIELD node, score
RETURN node.name, score
ORDER BY score DESC
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