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©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
The	Panama	Papers:	a	massive	leak
Image	VectorOpenStock
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
The	Panama	Papers:	a	massive	leak
11.5M	documents	
2.6TB	of	data
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
The	Panama	Papers:	a	massive	leak.
11.5M	documents	
2.6TB	of	data
@DataXDay@DataXDay
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
The	Panama	Papers:	a	massive	leak.
11.5M	documents	
2.6TB	of	data
@DataXDay@DataXDay
And	graphs	to	make	sense	of	it...
https://www.silicon.fr/linkurious-start-up-big-data-panama-papers-
144051.html?inf_by=5ae98d4c671db887218b5652
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
….	originating an	international	scandal
@DataXDay
Aurélia	Nègre
Data	Scientist
anegre@quantmetry.com
Alberto	Guggiola
Data	Scientist
aguggiola@quantmetry.com
Graph	Theory
… looking for	communities &	finding the	
leaders…
DataXDay
17th	May	2017
@DataXDay@DataXDay
Who are	we?
§ 70	Consultants	(Data	
Scientists,	Architects,	
Engineers,	Consultants	&	
more	…)
§ From proofs of	concept	to	
production
§ Fraud detection,	predictive
maintenance,	customer
insights	…
Aurélia	Nègre	&	Alberto	Guggiola
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
A	graph:	a	structure	made	up	of	nodes and	links
Social	network Transportation	network
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@DataXDay@DataXDay
Some use	cases	of	graph	theory
Spreading
• Determine the speed of a spreading
phenomenon
• How to speed it up or to slow it down?
Viral marketing, vaccination campaigns
Dynamics	&	optimisation
• Shortest path between two nodes?
• Effects of modifying the structure?
Transportation systems, social networks
Domino	effects
• Resilience to	random failures?	
• And	to	targeted attacks?
Security	systems,	economics,	
infrastructures
Structural	importance
• Which nodes are the most important or
authoritatives? Who are the leaders?
Google PageRank algorithm
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@DataXDay@DataXDay
Looking for	communities1
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@DataXDay@DataXDay
Community detection:	looking for	a	structure
Community:	Region having some degree of	autonomy ->	No	unique	formal definition!
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@DataXDay@DataXDay
Community detection:	looking for	a	structure
Community:	Region having some degree of	autonomy ->	No	unique	formal definition!
Which
communities
interact with each
other?
Which elements
act as	« bridges »	
between
communities?
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@DataXDay@DataXDay
Cutting the	bridges Gathering the	most
connected elements
Two approaches for	finding clusters
Spectral	clustering,	Girvan	Newman Fastgreedy,	Louvain,	Walktrap
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@DataXDay@DataXDay
Girvan	Newman:	a	good	algorithm on	small graphs	
(<500	nodes),	but	a	very high	complexity
Walktrap :	much more	efficient	on	large	graphs
Two examples
Random walk on	a	network:	path
following randomly chosen edges on	the	
graph
Community « strength »:	proportional to	the	
time	a	random walker spends inside it
Cut	the	bridges:	iteratively remove links	
with highest betweenness
Community are	found when the	graph	becomes
disconnected
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@DataXDay@DataXDay
✅ Able	to	identify heterogenous communities
✅ Efficient	on	large	graphs:	complexity O(N	logN)
✅ Available in	most graph	analytical libraries:	ok	as	first	try
And	the	winner	is...	Louvain	algorithm
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@DataXDay@DataXDay
✅ Able	to	identify heterogenous communities
✅ Efficient	on	large	graphs:	complexity O(N	logN)
✅ Available in	most graph	analytical libraries:	ok	as	first	try
And	the	winner	is...	Louvain	algorithm
Modularity optimization
Density of	edges inside vs	outside clusters
𝑄 =
1
2𝑚
& 𝐴() −
𝑘( 𝑘)
2𝑚
𝛿
	
()
(𝑐(, 𝑐))
Local	to	global	greedy
From
groups	of	
nodes …
…	to	groups	
of	clusters
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
• I	measure the	capability to	reconstruct real,	
known communities
• Example of	metrics:	Normalized Mutual
Information
I	observe	the	truth:	the	known communities
Testing the	algorithms and	measuring the	performances
I	create the	truth:	the	Stochastic Block	Model
• I	define the	probability for	each couple	of	
nodes to	be connected
• In	the	simplest	case:	
𝑝() = ?
𝐴	𝑖𝑓	𝑖, 𝑗	𝑖𝑛	𝑡ℎ𝑒	𝑠𝑎𝑚𝑒	𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦	
𝐵 < 𝐴	𝑖𝑓	𝑛𝑜𝑡
• More	links	inside communities as	a	
consequence
• Many observations	can be generated to	test	
algorithms
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
Look	at	modularity of	best	solution:	if	<0.3,	not	a	real	community structure
Possible	causes:
• On	generated	data,	intra	and	inter-community	probability	of	links	are	too	close
• On	real	networks,	the	known	communities	do	not	influence	the	structure
• The	approximated	solution	is	too	far	from	the	global	optimum
Possible	follow-up:	
• NLP	+	graphs:	groups	of	people	discussing	about	a	certain	topic
But	sometimes,	there is just no	pattern	to	be discovered …
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
Finding the	leaders2
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
Which node is the	most important?
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@DataXDay@DataXDay
Different ways of	measuring nodes importance	
A	global	importance	:	the	betweenness centrality A	local	importance	:	the	degree
Is	the	node « well connected »?
Count	its number of	direct	neighbours
Is	the	node a	« bridge »?	
Count	number of	shortest paths passing	through it
A well known,	iterative metric :	Google	PageRank	->	Is	the	node connected to	many important	nodes ?
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
Other	centrality	metrics
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@DataXDay@DataXDay
Can	provide	information	on	profiles	of	nodes
Combining centrality metrics &	identifiying hierarchies
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
And,	in	practice?3
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@DataXDay@DataXDay
Several tools,	depending on	your objectives
Non	distributed
analytical libraries
Distributed
analytical libraries
Databases
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
Free	networks	data	to	play with
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
@DataXDay@DataXDay
Demo Time	using LinkedIn	
data
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@DataXDay@DataXDay
• 3	blog	articles	(in	french):
– Introduction	à	une	théorie	aux	applications	multiformes	(Alberto	Guggiola)
– Détection	de	communautés	:	théorie	et	retour	d’expérience	(Aurélia	Nègre)
– Comment	identifier	les	rôles	stratégiques	des	influenceurs	d'un	réseau	?	(Ysé Wanono)
• https://www.quantmetry.com/blog
To	go	further...	
©	Quantmetry	2018	|	Diffusion	interdite	sans	accord
The video of this presentation
will be soon available at dataxday.fr
Thanks to our sponsors
Stay tuned by following @DataXDay

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