G R A P H T H E O R Y I N P R A CT I S E

D A V I D S I M O N S@ S W A M W I T H T U R T L E S

W H O A M I ?

David Simons

@SwamWithTurtles

github.com/SwamWithTurtles

Technical Lead at Softwire and part-time hacker

Statistician in a past life

T O S E E D ATA D O N E R I G H TM Y PA S S I O N

W H AT I S D ATA D O N E R I G H T ?

Choosing the right database;

Using the right mathematical and statistical techniques to leverage its power

S Q L

SQL has had 40 years of academic set theory applied to it

Lets do the same with neo4j!

T O D AY

Concepts in Graph Theory

Theory;

Use Cases;

Implementation Details

Reward: What shape is the internet?

W H AT I S A G R A P H ?G R A P H T H E O R Y

W H AT I S A G R A P H ?

Taken from Jim Webbers Dr. Who Dataset

W H AT I S A G R A P H ?

{ (V, E) : V = [n], E V(2) }

W H AT I S A G R A P H ?

{ (V, E) : V = [n], E V(2) }Made up of two parts,

V and E

W H AT I S A G R A P H ?

{ (V, E) : V = [n], E V(2) }V is a set of n items

W H AT I S A G R A P H ?

Vertex Set

W H AT I S A G R A P H ?

{ (V, E) : V = [n], E V(2) }E is made up of pairs

of elements of V(Ordered and

not necessarily distinct)

W H AT I S A G R A P H ?

Edge Set

G I V I N G R E A L W O R L D M E A N I N G S T O V A N D E

W H A T I S G R A P H I C A L M O D E L L I N G ?

B R I D G E S AT K N I G S B E R G

B R I D G E S AT K N I G S B E R G

V = bits of land

E = bridges

E L E C T I O N D ATA

E L E C T I O N D ATA

E L E C T I O N D ATA

E = (e.g.) member of, held in,

stood in

V = elections, constituencies,

years, politicians and parties

W H E R E D O E S N E O 4 J F I T I N ?

Stores both the vertex set and the edge set as first class objects:

Queryable

Can store properties

Typed

W H Y L E A R N T H E T H E O R Y ?

Tells us what we can do

Lets us utilise many years of academics

Gives us a common language

C A S E S T U D YT H E B R E A K D O W N

T H E B R I T I S H I S L E S

A G R A P H O F

W H AT I S A G R A P H ?

{ (V, E) : V = [n], E V(2) }

W H AT I S A G R A P H ?

{ (V, E) : V = Places of Interest,

E = Places that are connected }

T H E B R I T I S H I S L E S

L O N D O N

L A N D S E N D

O X F O R D

Y O R K

S T. I V E S

T H E B R I T I S H I S L E S

L O N D O N

L A N D S E N D

O X F O R D

Y O R K

S T. I V E S

P L A N A R I T Y

A planar graph is one that can be drawn on paper with its edges crossing

There are easy theories that tell you when a graph is planar

Used for planning construction of roads

C O N N E C T I V I T Y

A graph is connected if there is a path between any two points

A graph is k-connected if you need to remove at least k vertices to stop it being connected

Used for infrastructure robustness studies

S PA N N I N G T R E E

A tree is a graph with no loops

A spanning tree is a graph with tree with every vertex connected

Ensure resources flow through a network

C O L O U R I N GG R A P H T H E O R Y

W E L I K E T H E S I M P L E T H I N G S I N L I F E

M A T H E M A T I C I A N S

C O L O U R I N G I N

M A T H E M A T I C I A N S

C O L O U R I N G I N

Take your graph (V, E)

Vertex Colouring

Assign every vertex a colour such that no two adjacent vertices have the same colour.

T H AT S A L L V E R Y W E L L

O R G A N I S I N G S P O R T S T O U R N A M E N T S

W H Y ?

O R G A N I S I N G S P O R T S T O U R N A M E N T S

Graph Model

V = all matches that must be played

E = a team is the same across two matches

Two vertices the same colour => they can be played simultaneously

O R G A N I S I N G S P O R T S T O U R N A M E N T S

O R G A N I S I N G S P O R T S T O U R N A M E N T S

O T H E R U S E S

Mobile Phone Tower frequency assignment

V = mobile phone towers

E = towers so close their waves will interfere

Colours = frequencies

O T H E R U S E S

Solving SuDokus

V = Squares on a SuDoku grid

E = Knowledge that they must be different numbers

Colours = numbers 1 to 9

O T H E R U S E S

http://watch.neo4j.org/video/74870401Avoiding Deadlocks in Neo4j on Z-Platform

N O J AVA F R A M E W O R K Y E T !

R A N D O M G R A P H SG R A P H T H E O R Y

R A N D O M N E S S S E E M S C A RY

B U T WA I T

R A N D O M N E S S S E E M S C A RY

It can be!

Someone should do a talk about that

https://www.youtube.com/watch?v=rV9dqR0P0lQ

A graph with a fixed number of vertices, whose edges are generated non-deterministically

U S E C A S E SR A N D O M G R A P H S S T I L L H A V E

S T U B B E D T E S T D ATA

U S E C A S E S

S T U B B E D T E S T D ATA

Suppose you have a method that coloured the vertices of a graph

How could you test that?

S T U B B E D T E S T D ATA

S T U B B E D D ATA S E T

A P P LY M E T H O D

A S S E R T T H AT: * E V E R Y N O D E H A S A

C O L O U R * N O T W O A D J A C E N T

N O D E S S H A R E A C O L O U R

S T U B B E D T E S T D ATA

R A N D O M LY G E N E R AT E D D ATA S E T

A P P LY M E T H O D

A S S E R T T H AT: * E V E R Y N O D E H A S A

C O L O U R * N O T W O A D J A C E N T

N O D E S S H A R E A C O L O U R

S I M U L AT I O N A L G O R I T H M S

U S E C A S E S

- N A S D A Q . C O M

solving a problem by performing a large number of trail runs and inferring a solution from the

collective results of the trial runs.

W H Y S I M U L AT I O N ?

Modelling underlying randomness

Underlying question is impossible (or hard) to solve

Trying to model something of which we cannot have full knowledge

A N D

Its possible to use randomness and always be correct

cf. Probabilistic Combinatorics by Paul Erds

H O W C A N W E A C C O M P L I S H I T I N N E O 4 J ?

D I Y

I N T H E O R Y

D I Y

G R A P H A W A R E

I N P R A C T I S E

G R A P H A W A R E

#1 Neo4j Consultancy

Open-sourced a lot of projects under GPL3 including:

TimeTree

Reco

Algorithms

G R A P H A W A R E

G R A P H A W A R E

A graph with a fixed number of vertices, whose edges are generated non-deterministically

E R DS - R E N Y I

Take a graph with n vertices;

For each pair of vertices, randomly connect them with probability p

E R DS - R E N Y I

I W A N T T O M O D E L D ATA A B O U T K E V I N B A C O N

B U T

I W A N T T O M O D E L D ATA A B O U T S P R E A D O F H I V

B U T

I W A N T T O M O D E L D ATA A B O U T S C A L E F R E E N E T W O R K S

B U T

S C A L E F R E E N E T W O R K S

As the system grows, we have:

A small number of highly connected hubs

A large number of sparsely connected nodes

S C A L E F R E E N E T W O R K S

H U B S S PA R S E N O D E S

A C T O R C O W O R K E R S

Blockbuster stars, like Kevin Bacon

Drama college graduate #1828, #1829, #1830

S P R E A D O F H I V

PatriarchsLess privileged society

members

C H E M I C A L R E A C T I O N S

Catalysts Inert Chemicals

S C A L E F R E E N E T W O R K S

B A R A B A S I - A L B E R T

Take a graph with 2 (connected) vertices

Add vertices one at a time such that it is more likely to add vertices to a node that is already connected

Repeat until you have n vertices

B A R A B A S I - A L B E R T

Y O U R R E WA R DR E M E M B E R

I W A N T T O M O D E L D ATA A B O U T T H E I N T E R N E T

B U T

O V E R V I E W

Looking at graph theory can give us a common language

Utilising techniques means we dont have to solve problems from scratch each time (e.g. colouring, simulation)

The internet looks like Kevin Bacons career

A N Y Q U E ST I O N S ?

@ S W A M W I T H T U R T L E SS W A M W I T H T U R T L E S . C O M