Gremlin: A Graph-Based Programming Language

Gremlin G = (V, E)

A Graph-Based Programming Language
Marko A. Rodriguez
T-5, Center for Nonlinear Studies
Los Alamos National Laboratory
http://markorodriguez.com
http://gremlin.tinkerpop.com

February 25, 2010

Abstract
Gremlin is a Turing-complete, graph-based programming language
developed for key/value-pair multi-relational graphs called property graphs.
Gremlin makes extensive use of XPath 1.0 to support complex graph
traversals. Connectors exist to various graph databases and frameworks.
This language has application in the areas of graph query, analysis, and
manipulation.

Center for Nonlinear Studies PostDoc Seminar – Los Alamos National Laboratory – February 25, 2010

Acknowledgements
• Marko A. Rodriguez [http://markorodriguez.com]
designed, developed, tested, and documented Gremlin.
• Peter Neubauer [http://www.linkedin.com/in/neubauer]
aided in the design and the evangelizing of Gremlin.
• Pavel Yaskevich [http://github.com/xedin]
aided in the development of user deﬁned functions in Gremlin.
• Joshua Shinavier [http://fortytwo.net]
provided initial conceptual support for Gremlin.
• Ketrina Yim [http://csillustrated.berkeley.edu]
designed the logo for Gremlin.
• Gremlin-Users Group [http://groups.google.com/group/gremlin-users]
provided much direction in the design and implementation of Gremlin.


Outline

• Introduction to Graphs and Graph Software

• Basic Gremlin Concepts

• Gremlin Language Description

• Advanced Gremlin Concepts

• Conclusions


What is a Graph?
• A graph (network) is composed of a collection of vertices (dots) and edges (lines).
There are many types of graphs: directed/undirected, weighted, attributed, etc.

vertex-labeled

a
hyper
d edge-attributed
ed bele
ht e-la
multi

ig edgknows created=2-01-09
we 0.2 modiﬁed=2-11-09

cted
tic

undire
di
an

re
ct
m

hired ed
se

reg
ge

ula
half-ed

r
pseudo
http://ex.com/123
type="person"
name="emil" resource description framework

vertex-attributed


Why Use a Graph?

• A graph is a very general data structure that can be used to model
various systems.
A graph can model the structure of transportation, technological,
bibliographic, etc. systems.
A graph can model a list, a map, a tree, etc.

• There are numerous graph algorithms that are deﬁned independent of
the domain of the graph model.

• There are numerous graph databases, frameworks, packages, etc.
that aid in the creation, manipulation, and analysis of graphs.


Graph Databases, Frameworks, and Packages
• Neo4j Graph Database [http://neo4j.org]
• AllegroGraph Quad Store [http://http://www.franz.com/agraph]
• HyperGraphDB [http://www.kobrix.com/hgdb.jsp]
• Java Universal Network/Graph Framework [http://jung.sourceforge.net]
• OpenRDF Sesame Framework [http://www.openrdf.org]
• InfoGrid Graph Database [http://infogrid.org]
• Filament Graph Toolkit [http://filament.sourceforge.net]
• OWLim Semantic Repository [http://www.ontotext.com/owlim]
• Sones Graph Database [http://www.sones.com]
• NetworkX Graph Toolkit [http://networkx.lanl.gov]
• iGraph Toolkit [http://igraph.sourceforge.net]
• Blueprints Graph API [http://blueprints.tinkerpop.com]
• ... and many more.


What Makes Gremlin Diﬀerent?
• Gremlin is a domain speciﬁc language for working with graphs.

• Gremlin is not an application programming interface (API).

• Gremlin makes use of various graph databases, frameworks, packages.

• Gremlin is a language that currently has a virtual machine
implementation written in Java.

• What can be succinctly expressed in Gremlin is verbose/clumsy to
express in general purpose languages such as Java, Python, Ruby, etc.

• Gremlin allows one to map single-relational graph analysis algorithms
over to the multi-relational domain.


Single-Relational Graphs
• In single-relational graphs, all edges have the same meaning
(e.g. all edges are either frienship, kinship, worksWith, knows, etc.).
G = (V, E ⊆ (V × V ))

• Most graph algorithms are deﬁned for single-relational graphs
(e.g. centrality/ranking, clustering/community detection, etc.).

person-c

person-a person-b

NOTE: These types of graphs are also known as directed, vertex-labeled graphs.


Multi-Relational Graphs
• In multi-relational graphs, edges can have diﬀerent meanings.
G = (V, E ⊂ (V × V ), ω : E → Σ∗)

• Most graph software is designed for multi-relational graphs (e.g. arbitrary
objects as vertices and edges, knowledge-based reasoning systems, etc.).

book-c

read cites

person-a authored book-b

NOTE: These types of graphs are also known as directed, vertex/edge-labeled graphs.


Gremlin and Multi-Relational Graphs

• Gremlin provides a means to elegantly map single-relational graph
analysis algorithms over to the multi-relational graph domain.

• Gremlin provides an elegant way to do automated reasoning in
multi-relational graphs using path expressions.

These two points form the primary thesis of this presentation.

Rodriguez M.A., Shinavier, J., “Exposing Multi-Relational Networks to Single-Relational Network Analysis
Algorithms,” Journal of Informetrics, 4(1), 29–41, doi:10.1016/j.joi.2009.06.004, LA-UR-08-03931,
http://arxiv.org/abs/0806.2274, December 2009.


Property Graphs

• Gremlin works with a type of multi-relational graph called a property
graph.
Vertices and edges are labeled with unique identiﬁers.
Edges are directed, labeled, and can form loops.
Multiple edges of the same label can exist for the same vertex pair.
Vertices and edges can have any number of key/value pair
properties/attributes.

Property graphs are a relatively general graph structure that can be constrained to model other graph
structures — though, a property-based hypergraph would be the most general (see HyperGraphDB and the
JUNG API).


Property Graphs
name = "lop"
lang = "java"

weight = 0.4 3
name = "marko"
age = 29 created
weight = 0.2
9
1
created
8 created
12
7 weight = 1.0
weight = 0.4 6
weight = 0.5
knows
knows 11 name = "peter"
age = 35
name = "josh"
4 age = 32
2

10
name = "vadas"
age = 27
weight = 1.0

created

5

name = "ripple"
lang = "java"


Outline






Gremlin System Architecture

• The Gremlin console is a scripting environment
Gremlin Gremlin which allows for the dynamic evaluation of
Console ScriptEngine Gremlin code.
• Gremlin implements JSR 223 which allows
Gremlin to also be used within the Java
language and thus, as a virtual machine directly
accessible to Java applications. Popular JSR
223 implementations include Jython, JRuby, and
Groovy. For a ﬁne list of implementations see
https://scripting.dev.java.net.
• Blueprints is a set of interfaces for abstract
data structures such as graphs and documents.
Implementations to these interfaces exist for
various data management systems.
• There exist many graph data management
systems that span various graph data models
Neo4j NativeStore TinkerGraph (e.g. edge labeled graphs, RDF graphs,
hypergraphs, etc.).


“Hello World” in the Gremlin Console

marko$ ./gremlin.sh

,,,/
(o o)
-----oOOo-(_)-oOOo-----
gremlin>
gremlin> concat(‘goodbye’, ‘ ’, ‘self’)
==>goodbye self


Simple Traversals in Gremlin
name = "lop" gremlin> $_ := g:key(‘name’,‘marko’)
lang = "java"
==>v[1]
weight = 0.4 3
name = "marko"
age = 29 created
gremlin> .
1
9 ==>v[1]
created

7
8 created
12 gremlin> ./outE
6
weight = 0.5
knows
==>e[7][1-knows->2]
knows 11
weight = 1.0 ==>e[9][1-created->3]
name = "josh"
4
2
age = 32 ==>e[8][1-knows->4]
name = "vadas"
10 gremlin> ./outE/@weight
age = 27
==>0.5
created
==>0.4
5
==>1.0

./outE/@weight: “Get the current object(s). Then get the outgoing edges of those objects. Then get the
weights of those edges.”
$ is a reserved variable meaning the root list of objects.


name = "lop" gremlin> .
lang = "java"
==>v[1]
3
name = "marko" gremlin> ./outE[@label=‘created’]/inV
age = 29 created
9
==>v[3]
1 created

8 created
gremlin> $_ := $_last
12
7
6
==>v[3]
knows
knows
11
gremlin> ./@name
==>lop
4
2 gremlin> g:map(.)
10
==>name=lop
created
==>lang=java
5

./outE[@label=‘created’]/inV: “Get the current object(s). Then get the outgoing edges of those
objects, where their labels equal ‘created’. Then get the incoming vertices of those ‘created’ edges.”
$ last is a reserved variable meaning the last value evaluated.


name = "lop"
lang = "java"

3
name = "marko"
age = 29 created
9
1 created

8 created
12
7
6
knows
knows 11

name = "josh"
4 age = 32
2

10
name = "vadas"
age = 27

created

5

./outE[@label=‘knows’]/inV[matches(@name,‘va.{3}’) and @age > 21]/@name
==>vadas


./outE[@label=‘knows’]/inV[matches(@name,‘va.{3}’) and @age > 21]/@name

1. .: Get the current object(s).

2. outE[@label=‘knows’]: Get the outgoing edges of the current
object(s), where their labels equal ‘knows’.

3. inV[matches(@name,‘va.{3}’) and @age > 21]: Get the incoming
vertices of those ‘knows’ edges, where the names of those vertices are 5
characters long, start with ‘va’, and whose age is greater than 21.

4. @name: get the name of those particular incoming vertices.


Knowledge-Based Reasoning
• Blueprints implements the Sesame SAIL interfaces and thus, Gremlin
can be used over the many Resource Description Framework (RDF)
triple/quad stores. In such cases, RDF is modeled as a property graph
where the named graph component is the @ng edge property.

• Gremlin makes use of the Sesame SAIL SPARQL engine to allow for
queries based on graph-pattern matching.

gremlin> sail:sparql(‘SELECT ?x ?y WHERE { ?x foaf:knows ?y }’)
==>{y=v[http://ex.com#2], x=v[http://ex.com#1]}
==>{y=v[http://ex.com#4], x=v[http://ex.com#1]}

• Gremlin is useful for knowledge-based reasoning using path
expressions.


Reasoning as Deﬁning New Types of Adjacency
• Graph-based reasoning is the process
of making explicit what is implicit in
lop co-developer
the graph.
created

marko
created • A reasoner takes a graph G
co-developer
peter
and a collection of graph-patterns
created
(i.e. transformation/rewrite rules) and
knows knows
creates a new graph G (usually, G ⊂
josh
G ). G has new relationships/edges
vadas
and thus, new deﬁnitions of vertex
created adjacency.
• Example: The co-developers of person
ripple A are those people who have created
the same software as person A and who
are themselves, not person A (as person
For these “co-developer” examples, we will use
A has created the same software as him
vertex 1 (marko) as the source of the reasoning
or herself).
process.


The Co-Developers of Marko A. Rodriguez in SPARQL

name = "lop" SELECT ?x WHERE {
lang = "java"

?y
marko created ?y .
3
name = "marko"
age = 29 created
?z created ?y .
marko 1
created
?z ?z != marko .
created
6 ?z name ?x
knows
name = "peter" }
age = 35 ?x
knows
?z
4
name = "josh"
age = 32 ?x
This query would return: josh and
2
peter.
created

5


The Co-Developers of Marko A. Rodriguez in Gremlin
co-developer

lop co-developer
created
created
marko co-developer
peter
created

knows knows

josh
vadas

created

ripple

gremin> ./@name
==>marko
gremlin> ./outE[@label=‘created’]/inV/inE[@label=‘created’]/outV[g:except($_)]/@name
==>josh
==>peter


The Co-Developers of Marko A. Rodriguez in Gremlin

./outE[@label=‘created’]/inV/inE[@label=‘created’]/outV[g:except($_)]/@name

1. .: Get the current object(s) (i.e. vertex 1 — denoting Marko).
2. outE[@label=‘created’]: Get the outgoing edges of the Marko vertex, where their
labels equal ‘created’.
3. inV: Get the incoming (i.e. head) vertices of those ‘created’ edges.
4. inE[@label=‘created’]: Get the incoming edges of those vertices, where their
labels equal ‘created’.
5. outV[g:except($ )]: Get the outgoing (i.e. tail) vertices of those ‘created’ edges,
where those vertices are not the Marko vertex.
6. @name: get the name of those non-Marko vertices.


Deﬁning Co-Developers in Gremlin

path co-developer
./outE[@label=‘created’]/inV/inE[@label=‘created’]/outV[g:except($_)]
end

Once deﬁned, you can use it like any other path segment.
gremlin> ./co-developer
==>v[4]
==>v[6]
gremlin> ./co-developer/@name
==>josh
==>peter


Deﬁning Co-Developers in Java
public class CoDeveloperPath implements Path {
public List invoke(Object root) {
if(root instanceof Vertex) {
List<Vertex> projects = new ArrayList<Vertex>();
for(Edge edge : ((Vertex)root).getOutEdges()) {
if(edge.getLabel().equals("created")) {
projects.add(edge.getInVertex());
}
}
List<Vertex> coDevelopers = new ArrayList<Vertex>();
for(Vertex project : projects) {
for(Edge edge : project.getInEdges()) {
if(edge.getLabel().equals("created") && edge.getOutVertex() != root) {
coDevelopers.add(edge.getOutVertex());
}
}
}
return coDevelopers;
} else {
return null;
}
}
}


Gremlin Type System

object

element graph number string boolean map list

vertex edge


Predeﬁned Paths and Properties
vertex 1 out edges vertex 3 in edges
edge 9 out vertex edge 9 label edge 9 in vertex
edge 9 id

1 9 created 3

8 11
knows created
4 vertex 4 id
vertex 4 properties
name = "josh"
age = 32

object property description example
graph V the vertex iterator of the graph $g/V
graph E the edge iterator of the graph $g/E
vertex/edge @id the identiﬁer of the element $v/@id
vertex outE the outgoing edges of the vertex $v/outE
vertex inE the incoming edges of the vertex $v/inE
vertex bothE both in and out edges of the vertex $v/bothE
edge outV the outgoing tail vertex of the edge $e/outV
edge inV the incoming head vertex of the edge $e/outV
edge bothV both in and out vertices of the edge $e/bothV
edge @label the label of the edge $e/@label


Predeﬁned Functions

g:assign() g:remove-idx() g:list() g:sort() g:print()
g:assign() g:load() g:dedup() g:map() g:time()
g:unassign() g:save() g:union() g:keys() g:p()
g:id() g:clear() g:intersect() g:values() g:to-json()
g:key() g:close() g:difference() g:rand-nat() g:from-json()
g:add-v() g:keys() g:retain() g:rand-real() ...
g:add-e() g:values() g:except() g:prob() ..
g:remove-ve() g:map() g:remove() g:cont() .
g:idx-all() g:get() g:get() g:halt()
g:add-idx() g:op-value() g:op-value() g:type()

There are over 70 predeﬁned functions. See the following for a description of each.
http://wiki.github.com/tinkerpop/gremlin/core-function-library
http://wiki.github.com/tinkerpop/gremlin/gremlin-function-library


Working With Non-Graph Types
gremlin> 1.2 + 6
==>7.2
gremlin> ‘this is a string’
==>this is a string
gremlin> true() or false()
==>true
gremlin> g:map(‘marko’,‘lanl’,‘peter’,‘neotech’,‘josh’,‘rpi’)
==>marko=lanl
==>peter=neotech
==>josh=rpi
gremlin> g:list(‘graphs’,‘hockey’,‘motorcylces’,6)
==>graphs
==>hockey
==>motorcylces
==>6.0


Working With Non-Graph Types
gremlin> $m := g:map(‘hobbies’,g:list(‘hockey’,‘graphs’),
‘location’, g:map(‘state’,‘new mexico’, ‘city’, ‘santa fe’,
‘zipcode’, 87501), ‘age’, 30)
==>location={zipcode=87501.0, state=new mexico, city=santa fe}
==>age=30.0
==>hobbies=[hockey, graphs]
gremlin> $m/@age
==>30.0
gremlin> $m/@hobbies[2]
==>graphs
gremlin> $m/@location/@city
==>santa fe


Variables

• Variables in Gremlin are preﬁxed with a $ character.

• There are a collection of reserved variables that all begin with $ .
$ is the root list of objects.
$ last is the last result evaluated by the evaluator.
$ g is the “working graph” to reduce typing with graph functions.

gremlin> $x := 1
==>1.0
gremlin> $y := 2
==>2.0
gremlin> $x + $y
==>3.0


Language Statements
Variable Assignment Repeat

gremlin> $i := 0
gremlin> $i := 1 + 5 ==>0.0
==>6.0 gremlin> repeat 10
gremlin> $i $i := $i + 1
==>6.0 end
==>10.0
If/Else
While

gremlin> if true() gremlin> $i := ‘g’
$i := 1 ==>g
else gremlin> while not(matches($i, ‘ggg’))
$i := 2 $i := concat($i,‘g’)
end end
==>1.0 ==>ggg


Language Statements
Foreach Path

gremlin> $i := 0 gremlin> path friend_name
==>0.0 ./outE[@label=‘knows’]/inV/@name
gremlin> foreach $j in 1 | 2 | 3 end
$i := $i + $j gremlin> gremlin> ./friend_name
end ==>vadas
==>6.0 ==>josh
Function

gremlin> func ex:hello($name)
concat(‘hello ’, $name)
end
gremlin> ex:hello(‘pavel’)
==>hello pavel

You can deﬁne functions and paths in native Gremlin (as demonstrated above) or in Java.


XPath Filters

• Use [ ] ﬁlters to ﬁlter objects in a path expression (i.e. “such that” or
“where”)

• The evaluated result of [ ] must be a number or boolean.
If its a number, it is treated as the position within an array (i.e. list).
If it is boolean, it is treated as whether to include or exclude the
object from the next path in the sequence.

gremlin> ./outE[@label=‘knows’]
==>e[7][1-knows->2]
==>e[8][1-knows->4]
gremlin> ./outE[@label=‘knows’ and @weight>0.5]/inV[@age<21 or @name=‘josh’][true()][1]
==>v[4]


Outline





• Conclusion


A Grateful Dead Dataset

2,500 concerts
35,000 songs played
600 songs
30 years
11 members
1 band
... the Grateful Dead.


• vertices denote songs and artists
type: “song” or “artist”
name: name of song or artist.
performances: number of times song was
played in concert.
song type: whether the song was a “cover”
or “original”.

• edges denote followed by, sung by,
written by
weight: number of times a song was
followed by another song over all concerts
played.

Rodriguez, M.A., Gintautas, V., Pepe, A., “A Grateful Dead Analysis: The Relationship Between Concert and Listening

Behavior,” First Monday, 14(1), University of Illinois at Chicago Library, http://arxiv.org/abs/0807.2466, January 2009.

NOTE: A portion of the raw dataset courtesy of Mark Leone http://www.cs.cmu.edu/ mleone/gdead/setlists.html



Stanley Theater type="artist"
type="artist"
name="Hunter"
name="Garcia"
Pittsburgh, PA (11/30/79) type="song"
name="Scarlet.."
7
2nd Set 5
written_by 1 sung_by
-------------------
weight=239
Scarlet Begonias
followed_by type="song"
Fire on the Mountain name="Fire on.." sung_by sung_by
written_by
Passenger 2

Terrapin Station weight=1
type="artist"
name="Lesh"
... followed_by
type="song"
name="Pass.." 6
..
written_by 3 sung_by
.
followed_by
type="song"
weight=2 name="Terrap.."

4


A Grateful Dead Dataset – Load Data/Basic Stats

gremlin> g:load(‘data/graph-example-2.xml’)
==>true
gremlin> count($_g/V)
==>809.0
gremlin> count($_g/E)
==>8049.0


A Grateful Dead Dataset – Out-Degree of Each Vertex

gremlin> $degrees := g:map()
gremlin> foreach $v in $_g/V
$degrees[@name=$v/@name] := count($v/outE)
end


A Grateful Dead Dataset – Out-Degree of Each Vertex

gremlin> g:sort($degrees, ‘value’, true())
==>PLAYING IN THE BAND=96.0
==>SUGAR MAGNOLIA=92.0
==>PROMISED LAND=89.0
==>GOOD LOVING=87.0
==>NOT FADE AWAY=86.0
==>I KNOW YOU RIDER=85.0
==>CASSIDY=83.0
==>DEAL=82.0
==>JACK STRAW=81.0
==>ONE MORE SATURDAY NIGHT=81.0
==>EL PASO=80.0
==>MEXICALI BLUES=79.0
...


A Grateful Dead Dataset – Inspecting Single Vertex

gremlin> $v := g:key(‘name’,‘CHINA DOLL’)[1]
==>v[129]
gremlin> g:map($v)
==>name=CHINA DOLL
==>song_type=original
==>performances=114
==>type=song
gremlin> $v/outE[@label=‘sung_by’]/inV/@name
==>Garcia


A Grateful Dead Dataset – Inspecting Single Vertex
gremlin> $v/outE[@label=‘followed_by’]/inV/@name
==>BIG RIVER
==>THROWING STONES
==>SAMSON AND DELILAH
==>TRUCKING
==>CASEY JONES
==>HIGH TIME
...
gremlin> $v/outE[@label=‘followed_by’]/@weight
==>2
==>8
==>1
==>2
==>1
==>1
...


Introduction to PageRank
• The remainder of this section will discuss the PageRank algorithm and
its application to multi-relational graphs.

• The arguments made and the examples presented generalizes to all other
single-relational graph algorithms. However, for the sake of brevity and
consistency, only PageRank will be discussed.


Introduction to Matrix-Based PageRank

• PageRank is a centrality measure based on the primary eigenvector
|V |×|V |
of a modiﬁed version of a graph. Let A ∈ R+ denote the
adjacency matrix representing the graph.

• In order to ensure a positive real values in the eigenvector, the graph
must be strongly connected. PageRank induces strong connectivity
by overlaying a low probability (deﬁned by α ∈ [0, 1] – usually 0.15)
1 |V |×|V |
“teleportation” graph over the original graph. Let B ∈ |V | denote
a teleportation adjacency matrix where ever vertex is connected to vertex
with equal probability.
|V |×|V |
C = (1 − α)A + αB, where C ∈ R+
|V |
λ = λC, where λ ∈ R+ is the PageRank vector over V .


Introduction to Random Walk-Based PageRank
• PageRank can be implemented by a random walk.

• Create a vertex counter map, m : V → N+.

• Place a walker on a random vertex in V . Denote the walker’s current
vertex i ∈ V .
1. increment the vertex counter by 1 (i.e. m(i) ← m(i) + 1).
2. the walker chooses a random adjacent vertex with probability α.
3. the walker chooses a random vertex in V with probability 1 − α.
4. rinse and repeat until m reaches a stationary probability distribution
(continually normalize m if you want a probability distribution).

• We will use this random walk model in the Gremlin examples to follow.


PageRank over Multi-Relational Graphs

• PageRank was designed for single-relational graphs (i.e. where all edges
have the same meaning).

• In a multi-relational graph, what does it mean to ﬁnd the centrality
of a vertex when vertices can be related by various types of edges?
For example, if there exists “socializes with” and “met once”, then the
person who “met once” many people could be the most centrally located
in the graph. Also, what if you graph has more than just “person”-type
vertices (e.g. cars, pets, buildings, articles, etc.) and “person”-type
edges (e.g. owns, walks, livesAt, cites, etc.).


• Calculating single-relational PageRank
would yield Person as the most central ...
Person type
vertex. type
type
• You can boolean ﬁlter certain edge labels type
type
(e.g. ignore type edges — in such cases, type
type type type type type type
you would have the centrality scores over
the knows social graph).
• However, what if you only wanted to
traverse knows edges if and only if the Herbert Johan Marko Josh Jen ...
adjacent vertex knows more than 10
other people? knows knows knows knows

• In the end, you want complete
knows knows
control (universal computability)
over the paths that the
traverser/walker can take through
a graph.


• In multi-relational graphs, the meaning of your graph algorithm’s results are
defined by your definition of adjacency.
• With respect to random walk-based PageRank, define the path that the walker
should take. That path is the definition of adjacency.
• The stationary probability distribution created from this walk yields a path-dependent
centrality.
• Thus, in a multi-relational graph, there are many types of PageRanks that can
be calculated — one for each type of path defined for a walker.

Rodriguez, M.A., “Grammar-Based Random Walkers in Semantic Networks”, Knowledge-Based Systems,
21(7), 727–739, http://arxiv.org/abs/0803.4355, October 2008.


PageRank over “Garcia Followed By” SubGraph

• Deﬁne a path that will go from song-to-song by “followed by” edges and
only traverse songs that are “sung by” Jerry Garcia.

(./outE[@label=‘followed_by’]/inV/outE[@label=‘sung_by’]
/inV[name=‘Garcia’]/../..)[g:rand-nat()]

A B C D /../..
followed_by sung_by name="Garcia"

g:rand-nat()
. followed_by sung_by name="Garcia"

followed_by sung_by name="Weir"


path garcia-followed_by
(./outE[@label=‘followed_by’]/inV/outE[@label=‘sung_by’]
/inV[name=‘Garcia’]/../..)[g:rand-nat()]
end

$m := g:map()
$alpha := 0.15
$_ := g:key(‘type’, ‘song’)[g:rand-nat()]
repeat 2500
$_ := ./garcia-followed_by
if count($_) > 0
g:op-value(‘+’,$m,$_[1]/@name, 1.0)
end
if g:rand-real() < $alpha or count($_) = 0
$_ := g:key(‘type’, ’song’)[g:rand-nat()]
end
end


gremlin> g:sort($m,‘value’,true())
==>CRAZY FINGERS=98.0
==>HES GONE=85.0
==>CHINA CAT SUNFLOWER=79.0
==>BERTHA=76.0
==>UNCLE JOHNS BAND=74.0
==>TERRAPIN STATION=72.0
==>GOING DOWN THE ROAD FEELING BAD=71.0
==>WHARF RAT=71.0
==>EYES OF THE WORLD=65.0
==>COLD RAIN AND SNOW=62.0
==>SHIP OF FOOLS=58.0
==>RAMBLE ON ROSE=53.0
==>CASEY JONES=51.0
==>DARK STAR=47.0
==>DEAL=46.0
...


Universal Computation in Paths
path path-name
# any arbitrary computation can occur here
end

• A path definition can be used to define adjacencies.
adjacency can be expressed as anything that can be computed by a Turing machine.
path definitions are used to create “semantically meaningful” results from single-
relational graph algorithms applied to multi-relational graphs.
path definitions make explicit what is implicit in the structure of the graph. This
has applications to knowledge-based reasoning.
• A path definition can perform any arbitrary computation.
path definitions can check/set vertex/edge properties.
path definitions can create new vertices and edges.
path definitions can call/define functions.

This allows fine grained control over how your traverser/walker moves through a graph.


The Current Gremlin EcoSystems
• Webling: Web console for Gremlin
(developed by Pavel Yaskevich w/ funding from Neo Technology)

Webling
• Project Gargamel: Distributed Graph Computing
(uses Linked Process and Gremlin)

• ReXster: A Graph-Based Recommender Engine


Thank You
Please enjoy Gremlin at http://gremlin.tinkerpop.com ...

My homepage is http://markorodriguez.com.
Please feel to contact me with any questions or comments.


Gremlin: A Graph-Based Programming Language

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