Intro to advanced graph analytics and two novel applications in health care. Presented at the 2018 SOA Annual Meeting.

1. Social Network Analysis
for Healthcare and
Finance
John C. Liu, Ph.D. CFA

2. Disclaimer
The information set forth herein has been obtained or derived from sources generally
available to the public and believed by the author to be reliable, but the author does not
make any representation or warranty, express or implied, as to its accuracy or completeness.
The information is for educational purposes only and are not intended to be used as the
basis of any business or investment decisions by any person or entity. This information does
not constitute investment advice, nor is it an offer or a solicitation of an offer to buy or sell
any security. This presentation should not be considered to be a recommendation by any
individual affiliated with CFA Society Nashville or the CFA Institute.

3. WHAT WE DO
Cognitive Computing
platform that understands
human communication
OFFICE LOCATIONS:
Nashville
Washington
New York
London
INVESTORS:
Goldman Sachs, Credit
Suisse, Nasdaq, In-Q-Tel,
HCA & Lemhi Ventures
RESULTS PROVEN IN:
Government
Financial Services
Health Care
Data Science
STRATEGIC PARTNERS
THE POWER TO IMPROVE YOUR WORLD
THE PATH TO AN INTELLIGENT ENTERPRISE

4. Definitions

5. Clique
clique
a subset S of the vertices is
a clique if there is an edge
between every pair of
vertices in S

6. Triangle Count
triangle count
counting the triangles that
pass through each vertex
measures the strength of
communities
Lower Count = weaker communities
Higher Count = stronger communities

7. Adjacency Matrix
Adjacency matrix
matrix representation of a
graph where each element
represents an edge between
nodes

8. Cascade
cascade
The process of spreading
from one node to another
generates a propagation
graph

9. Diffusion
diffusion
Process of contagion in
social networks where
information spreads through
cascades
Leskovec & Faloutsos, WWW 2008

10. Diffusion Curve
Leskovec & Faloutsos, WWW 2008
Shape matters! Diminishing returns? Critical Mass?

11. Virus Propagation
Virus birth rate 𝛃
Virus death rate 𝛿
Virus strength 𝛃/𝛿
Leskovec & Faloutsos, WWW 2008

12. Epidemic Threshold 𝜏
No epidemic if 𝛃/𝛿 < 𝜏, but what should 𝜏 depend on?
● Average out-degree? Highest out-degree?
● Variance of out-degree?
● Skew of out-degree?
● Diameter?
Leskovec & Faloutsos, WWW 2008

13. http://doi.acm.org/ 10.1145/1284680.1284681Threshold Theorem

14. Contagion Model
Activations cascade through the network
Domingos & Richardson 2001

15. Most Influential Subset
Most influential subset
set of k nodes producing
largest expected cascade
size if activated
Domingos & Richardson 2001

16. Disease Progression

17. Disease Prediction
Framework
Longitudinal Distance
Matching = Subgraph
Similarity
Khan, Uddin, Srinivasan (2016)

18. Baseline network for
Progression of
Type-2 Diabetes
Size of a node represents
total number of other ICD
codes that lead to or
occurred from that node.
Khan, Uddin, Srinivasan (2016)

19. Healthcare Fraud

20. Healthcare Fraud
Risk Estimation
Provider Payment Data (PUF)
Provider Drug Data (Part-D)
National Provider Identifiers (NPIs)
Branting, Reeder, Gold, Champney (2016)

21. Risk Estimation Branting, Reeder, Gold, Champney (2016)
Behavior-based estimation Risk propagation

22. Q & A

23. Thank you.
Power to Improve Your World