Labyrinth

Package to compute network parameters

Trying to recreate some of the functionality of neo4j mazerunner project, the handy work of @kennybastani. I think that the capabilities are very cool. They utilize Neo4j, Docker and GraphX for each of their strengths. For smaller networks though this can be a bit much. I have created something similar using igraph.

You first need the newest version of RNeo4j.

devtools::install_github("darrkj/labyrinth")

require(labyrinth)

Create connection to graph database

graph <- startGraph("http://localhost:7474/db/data/")

Run this for the example to work.

if(!exists('ingest')) {
  devtools::source_url("https://raw.githubusercontent.com/darrkj/RNeo4j/master/R/ingest.R")
}

This will add the commonly known Karate data to Neo4j

require(igraphdata)
data(karate)

ingest(karate, 'knows')

Check out what the data looks like.

getNodes(graph, 'match n where n.domain = "karate" return n')[1]

Nothing more than the data that was included from the Karate data set.

## $Faction
## [1] 1
## 
## $domain
## [1] "karate"
## 
## $name
## [1] "Mr Hi"

In the labyrinth project there is a function called mazewalker which can add network statistics to nodes in Neo4j.

We can choose any number of local or global network measures. The first argument is a way to specify a particular sub-graph. Of you ignore it you just get the whole database.

mazeWalker('karate', 
           loc = c("degree", 'closeness', 'hub', 'eigenvector', 'page'), 
           glob = c('Assortativity', 'Clique', 'Diameter', 'Girth', 'Adhesion'))

Now we can re-query the database to see the new parameters that have been added.

getNodes(graph, 'match n where n.domain = "karate" return n')[1]

Lots of interesting things have appeared.

## $domain
## [1] "karate"
## 
## $name
## [1] "Mr Hi"
## 
## $Faction
## [1] 1
## 
## $Adhesion
## [1] "0"
## 
## $Girth
## [1] "3"
## 
## $Diameter
## [1] "5"
## 
## $Clique
## [1] "5"
## 
## $Assortativity
## [1] "-0.475613097684614"
## 
## $page
## [1] "0.0484986426941474"
## 
## $eigenvector
## [1] "0.952132366476656"
## 
## $hub
## [1] "0.952132366476656"
## 
## $closeness
## [1] "0.0282700421940928"
## 
## $degree
## [1] "32"

Global Measures

• Degree
• Deg_Cent
• Clos_Cent
• Bet_Cent
• Eig_Cent
• Assortativity
• Avg_Path_Len
• Clique - Maximal Clique Size
• Diameter
• Radius
• Girth
• Adhesion
• Density
• Chordal
• Connected - Boolean, is the graph connected

Local Measures

• degree
• closeness
• betweeness
• eigenvector
• hub
• auth
• page