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Social	Network	Analysis:	
Applications	&	Challenges
Summer	School	on	Social	Computing	
June	30,	2018
Ponnurangam	Kumaraguru	(“PK”)
IIIT	{D,	H}	
TEDx &	ACM	Distinguished	Speaker
Linkedin/in/ponguru/	
fb/ponnurangam.kumaraguru
@ponguru
2
Manish’s	Lecture	
– Clustering	coefficients	
– Power	law	
– Small	world	
– Kleinberg’s	model
– ….		
3
4
Growth	of	Social	Media	
5
Top	sites	Alexa…
6
7
2009
8
9
10
What	is	available	in	social	media?	
11
What	is	available	in	social	media?	
12
Potential	applications
– Recommend	friends	/	WTF	
– Analyze	and	improve	information	/	
communication	flow		
– Identify	criminal	and	terrorist	networks	
– Identify	influencers	(Manish	will	also	cover)	
– Finding	the	hidden	connections	
-Shadow	profiles		
– Optimize	the	structure	and	capacity	of	
telephone	/	mobile	networks	
– …..	
13
Centrality	measures	
– Degree	
-How	many	to	reach	directly?	
-E.g.	How	many	have	collaborated	 with	Prof.	
Pushpak?	How	many	has	he	collaborated	 with?	
– Betweenness
-Connecting	directly	2	different	people	/	networks	
-E.g.	Arts	&	CS
– Closeness	
-How	quickly	can	this	node	reach	all	nodes?
-Information	given,	to	reach	among	all	participants	
– Eigenvector	
-How	well	connected	to	other	well	connected	nodes	
-Well	cited	author	who	is	cited	by	other	well	cited	
authors	
14
To	understand	users	/	customers	
– “Show	me	who	your	friends	are	and	I’ll	tell	
you	who	you	are?”	
15
To	understand	users	/	customers	
– “Show	me	who	your	friends	are	and	I’ll	tell	
you	who	you	are?”	
16
To	understand	users	/	customers	
– “Show	me	who	your	friends	are	and	I’ll	tell	
you	who	you	are?”	
17
To	understand	users	/	customers	
– Interest	graph
18
WTF	/	Recommendations….	
19
20
Recommendations	/	Advertising
21
Recommendations	/	Advertising
Recommendations	/	Advertising	
22
Information	flow?	
23
faculty@lists.iiit.ac.in
faculty@iiitd.ac.in
Security	applications	
24
Leader?	 2nd in	command?
Predicting	Churning	
– What	is	Churning?
-"Customer	churn	is	nothing	but	change	and	turn	
in	which	customer	terminates	the	existing	
service	and	joins	service	provided	by	other	
service	providers.	By	analyzing	the	social	
networks	and	activities	of	customers	on	social	
networks	we	can	predict	the	churning	
customers."	
– Examples	
-Leaving	Airtel	and	going	to	Vodafone	
-Leaving	Netflix	and	going	to	Amazon	Prime
25
Wordnet (Dr.	Pushpak’s lecture)	
26
Wordnet (Dr.	Pushpak’s lecture)	
27
28
29
Solving	Societial problems	
30
Innovation
31
Productivity	
32
Depression	
33
Answers	
34
Answers	
35
Movies	
36
37
38
39
What	is	the	problem	with	this?	
40
Challenges	
– Very	hard	to	collect,	store,	and	analyze	data	
-Rate	limits	
-Large	size	
-Ego-network	
41
Challenges	
– Understanding	users	/	modeling	their	
interests,	behavior	
-Manipulated	profile	information	
-Multiple	accounts	
42
Challenges	
– Understanding	the	content	/	meaning	
-Sentiment	
-Sarcasm	
-Emojis
-Code-mixed	
-….	
43
Challenges	
– Fake	/	Bot	vs	Real	
44
Challenges	
– Fake	/	Bot	vs	Real	
45
Challenges	
– Who’s	Who	/	Identity	in	multiple	networks	
46
47
https://www.facebook.com/ponnurangam.kumaraguru
48
https://twitter.com/ponguru
49
https://www.linkedin.com/in/ponguru/
De-duplicating	audience
Social	audience		=	437,632	+	153,000	+	805,097	or	less??
50
Takeaways	
– Social	Network	Analysis	is	a	power	tool	
– Business	/	Government	can	use	the	analysis	
to	their	benefits	
– You	should	use	it	for	your	benefits	J
-You	tried	doing	analysis	on	your	accounts?	
– Lots	of	challenges	and	opportunities!	
– Will	be	happy	to	interact	more,	if	needed	
51
52
pk@iiitd.ac.in	
http://precog.iiitd.edu.in/	
https://fb.com/ponnurangam.kumaraguru
@ponguru
Linkedin/in/ponguru/

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