Social network plays a fundamental role as a medium for the spread of INFLUENCE among its members. As part of this research, estimates for influence between individuals were presented.

1. KEY INFLUENCERS
IN
SOCIAL NETWORKS
Faraz Zaidi PhD.
Data Mining and Visual Analytics

2. PROFILE
 Advisor Health Analytics,
Region of Peel
 Data Scientist Consultant,
Piik Insights Inc.
 Adjunct Faculty,
PAF-KIET University

3. KEY INFLUENCERS IN SOCIAL NETWORKS

4. KEY INFLUENCERS IN SOCIAL NETWORKS

5. WHAT IS A NETWORK, OR A GRAPH?

6. CO-AUTHOR NETWORK
3621 nodes
9461 edges
Force Directed Algorithm - Hachul and Junger, Symp. on Graph Drawing, 2004

7. SOURCE CODE – DEPENDENCIES
SolidSource: http://www.solidsourceit.com/products/SolidSX-source-code-dependency-
analysis.html

8. FOOD WEB- WHO EATS WHO?
https://k8schoollessons.com/food-chains-food-webs/

9. TEXT-WORD ASSOCIATIONS
The large-scale structure of semantic networks: statistical analyses and a model of
semantic growth, M. Steyvers, J. B. Tenenbaum (2005) Cognitive Science, 29(1)
Free word
associations

10. WHAT IS SOCIAL INFLUENCE?
 Social influence occurs when one's
opinions, emotions, or behaviors
are affected by others, intentionally or
unintentionally

12. SOCIAL NETWORK AND SPREAD OF INFLUENCE
 Social network plays a fundamental
role as a medium for the spread of
INFLUENCE among its members
 Opinions, emotions, behavior,
innovation, ideas, innovation…
 Direct Marketing takes the “word-of-mouth”
effects to significantly increase profits

13. SPREAD OF INFLUENCE:
TWO OTHER APPLICATIONS
 Preventing large scale epidemic
through identification of optimal
influential spreaders
 Protecting interconnected infrastructures through
identification of influential hubs: cities, airports,
roads etc

14. 95000
Flights
Cancelled!

15. PROBLEM SETTING - MARKETING
 Given
 a limited budget for initial advertising (e.g. give away
free samples of product)
 estimates for influence between individuals
 Goal
 trigger a large cascade of influence (e.g. further
adoptions of a product)
 Question
 Which set of individuals should we target?

16. EVALUATE INFLUENCERS?
 Common Approaches:
 Simulation Models
Independent Cascade/ Linear Threshold
 Network Connectivity
Size of Biggest Connected Component

17. NETWORK CONNECTIVITY

18. METHODS TO IDENTIFY INFLUENTIAL
NODES

19. METHODS AND ALGORITHMS
 Degree/Adaptive Degree
 Page Rank
 Betweeness/Adaptive Betweeness Centrality
 Greedy
 CI

20. DEGREE
1
23

21. ADAPTIVE DEGREE

22. ADAPTIVE DEGREE
1
32

23. PAGE RANK
 Counting the number and quality of links to a page
to determine how important the website is.

24. BETWEENESS / ADAPTIVE BETWEENESS
1
3
2
Freeman, L. C. A Set of Measures of Centrality Based on Betweenness Sociometry,
1977, 40, 35-41

25. GREEDY APPROACH
Kempe, David, Jon Kleinberg, and Éva Tardos. "Maximizing the spread of influence
through a social network." Proceedings of the ninth ACM SIGKDD, 2003.

26. CI – COLLECTIVE INFLUENCE
 Most influential nodes
 High speed calculation
 Low Complexity
 Suitable for huge networks

27. CI – COLLECTIVE INFLUENCE

28. CODE

29. NETWORKS OF CITIES
 Worlds top 3000 Group of Companies and their
800,000 subsidiaries
 Economic drivers of growth
 Location: Cities
 Identify influential cities around the world

30. NETWORKS OF CITIES

31. COMPARISON OF METRICS

32. CONCLUSIONS
 Some promising methods have been discovered
 Still lot of potential to improve and find new ways
 Numerous applications to real world problems