Speaker: Dr. David A. Bader, Distinguished Professor and Director of the Institute for Data Science, New Jersey Institute of Technology

1. Using Graphs to Enable
National-Scale Analytics
http://www.cs.njit.edu/~bader
Connections:
Graphs in Government

2. David A. Bader
Distinguished Professor and
Director, Institute for Data Science
• IEEE Fellow, SIAM Fellow, AAAS Fellow
• Recent Service:
• White House's National Strategic Computing Initiative (NSCI) panel
• Computing Research Association Board
• NSF Advisory Committee on Cyberinfrastructure
• Council on Competitiveness HPC Advisory Committee
• IEEE Computer Society Board of Governors
• IEEE IPDPS Steering Committee
• Editor-in-Chief, ACM Transactions on Parallel Computing
• Editor-in-Chief, IEEE Transactions on Parallel and Distributed Systems
• Over $184M of research awards
• 250+ publications, ≥ 9,700 citations, h-index ≥ 57
• National Science Foundation CAREER Award recipient
• Directed: Facebook AI Systems
• Directed: NVIDIA GPU Center of Excellence, NVIDIA AI Lab (NVAIL)
• Directed: Sony-Toshiba-IBM Center for the Cell/B.E. Processor
• Founder: Graph500 List benchmarking “Big Data” platforms
• Recognized as a “RockStar” of High Performance Computing by InsideHPC in 2012 and as
HPCwire’s People to Watch in 2012 and 2014.
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3. Solving real-world challenges
• Urban sustainability
• Healthcare analytics
• Trustworthy, Free and Fair Elections
• Insider threat detection
• Utility infrastructure protection
• Cyberattack defense
• Disease outbreak and epidemic monitoring
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4. Strategic Intelligence
4
“Advances in communications and the
democratization of other technologies have also
generated an ability to create and share vast and
exponentially growing amounts of information farther
and faster than ever before. This abundance of data
provides significant opportunities for the IC, including
new avenues for collection and the potential for
greater insight, but it also challenges the IC’s ability to
collect, process, evaluate, and analyze such enormous
volumes of data quickly enough to provide relevant
and useful insight to its customers.”
à “Develop and maintain capabilities to acquire and
evaluate data to obtain a deep understanding of the
global political, diplomatic, military, economic,
security, and informational environment. “
15 September 2020 David Bader

5. Data Science:
Discovery and Innovation
The National Strategic Computing
Initiative (NSCI) The NSCI was
launched by Executive Order (EO)
13702 in July 2015 to advance U.S.
leadership in high performance
computing (HPC).
McKinsey predicts that data-driven technologies will
bring an additional $300 billion of value to the U.S.
health care sector alone, and by 2020, 1.5 million more
“data-savvy managers” will be needed to capitalize on
the potential of data, “big” and otherwise.
Manyika, J. et al. (2011). Big data: The next frontier for innovation,
competition, and productivity. McKinsey Global Institute. Retrieved from
http://www.mckinsey.com/business-functions/business-technology/our-
insights/big-data-the-next-frontier-for-innovation
The ability to manipulate data and understand Data Science
is becoming increasingly critical to current and future
discovery and innovation.
REALIZING THE POTENTIAL OF DATA SCIENCE Final Report from
the National Science Foundation Computer and Information
Science and Engineering Advisory Committee Data Science
Working Group. Francine Berman and Rob Rutenbar, co-Chairs
Henrik Christensen, Susan Davidson, Deborah Estrin, Michael
Franklin, Brent Hailpern, Margaret Martonosi, Padma Raghavan,
Victoria Stodden, Alex Szalay. December 2016
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6. National Strategic Computing
Initiative (NSCI) Update
14 Nov 2019
• Computing hardware, with a
focus on the 10-year horizon
and beyond;
• Software infrastructure that
will enable effective and
sustainable use of new
computing;
• Overall infrastructure, from
data usage and management to
cybersecurity, foundries, and
prototypes;
• And the development of new
real-world applications,
systems, and opportunities for
future computing.
In recognition of the fast-changing computing
landscape, the updated plan places new emphasis on
the following areas as compared to the 2016 plan:
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7. The Reality
• This image is a
visualization of a
personal friendster
network (circa
February 2004) to 3
hops out. The
network consists of
47,471 people
connected by
432,430 edges.
Credit: Jeffrey Heer, UC Berkeley
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8. 8
Advantages of Graph Analytics
• Much smaller than raw data. Can fit in memory of
large computer
• Fast response to queries
• Pre-join of database
• Combine data from different sources and of
different types
• Some common intelligence and law enforcement
queries are naturally posed on graphs
• Particularly for the terrorist threat
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9. Query Example I: Short Paths
David Bader 9

10. Query Example II: Motif Finding
Image Source:
T. Coffman,
S. Greenblatt,
S. Marcus,
Graph-based
technologies for
intelligence
analysis,
CACM, 47
(3, March 2004):
pp 45-47
David Bader 10

11. The Big Picture
Analyst makes
queries.
Massive
Databases
Fast
Graph
Query
Extract “Window”
High Latency Query
Graph resides in memory.
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12. Data-Quad
Known
Known
Unknown
Unknown
Objects
Patterns
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13. Graph Data Science: Real-world challenges
All involve exascale streaming graphs:
• Health care à disease spread, detection and prevention of
epidemics/pandemics (e.g. SARS, Avian flu, H1N1 “swine” flu)
• Massive social networks à understanding communities, intentions,
population dynamics, pandemic spread, transportation and evacuation
• Intelligence à business analytics, anomaly detection, security, knowledge
discovery from massive data sets
• Systems Biology à understanding complex life systems, drug design,
microbial research, unravel the mysteries of the HIV virus; understand life,
disease,
• Electric Power Grid à communication, transportation, energy, water, food
supply
• Modeling and Simulation à Perform full-scale economic-social-political
simulations
REQUIRES PREDICTING / INFLUENCE CHANGE IN REAL-TIME AT SCALE
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14. Graphs are pervasive in large-scale data analysis
• Sources of massive data: peta- and exa-scale simulations, experimental
devices, the Internet, scientific applications.
• New challenges for analysis: data sizes, heterogeneity, uncertainty, data
quality.
Astrophysics
Problem: Outlier detection.
Challenges: massive datasets,
temporal variations.
Graph problems: clustering,
matching.
Bioinformatics
Problem: Identifying drug target
proteins.
Challenges: Data heterogeneity,
quality.
Graph problems: centrality,
clustering.
Social Informatics
Problem: Discover emergent
communities, model spread of
information.
Challenges: new analytics routines,
uncertainty in data.
Graph problems: clustering,
shortest paths, flows.
Image sources: (1) http://physics.nmt.edu/images/astro/hst_starfield.jpg
(2,3) www.visualComplexity.com15 September 2020 David Bader 14

15. 15 September 2020 David Bader 15

16. Massive Data Analytics: Infrastructure
• The U.S. high-voltage transmission
grid has >150,000 miles of line.
• Real-time detection of changes and
anomalies in the grid is a large-scale
problem.
• May mitigate impact of widespread
blackouts due to equipment failure or
intentional damage.
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17. Network Analysis for Intelligence and Surveillance
• [Krebs ’04] Post 9/11 Terrorist
Network Analysis from public domain
information
• Plot masterminds correctly identified
from interaction patterns: centrality
• A global view of entities is often more
insightful
• Detect anomalous activities by
exact/approximate graph matching
Image Source: http://www.orgnet.com/hijackers.html
Image Source: T. Coffman, S. Greenblatt, S. Marcus, Graph-based technologies
for intelligence analysis, CACM, 47 (3, March 2004): pp 45-47
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18. Massive Data Analytics: Public Health
• CDC/national-scale surveillance of public health
• Cancer genomics and drug design
• Computed Betweenness Centrality of Human Proteome
Human Genome core protein interactions
Degree vs. Betweenness Centrality
Degree
1 10 100
BetweennessCentrality
1e-7
1e-6
1e-5
1e-4
1e-3
1e-2
1e-1
1e+0
ENSG000001
45332.2
Kelch-like
protein
implicated in
breast cancer
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19. Massive Streaming Graph Analytics
(A, B, t1, poke)
(A, C, t2, msg)
(A, D, t3, view wall)
(A, D, t4, post)
(B, A, t2, poke)
(B, A, t3, view wall)
(B, A, t4, msg)
Billions of nodes
… n9 n8 n7 n6 n5 n4 n3 n2 n1 …
Analysts
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20. Centrality in Massive Social Network Analysis
• Centrality metrics: Quantitative measures to capture the
importance of person in a social network
• Betweenness is a global index related to shortest paths that
traverse through the person
• Can be used for community detection as well
• Identifying central nodes in large complex networks is the
key metric in several applications:
• Biological networks, protein-protein interactions
• Sexual networks and AIDS
• Identifying key actors in terrorist networks
• Organizational behavior
• Supply chain management
• Transportation networks
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21. Betweenness Centrality (BC)
• Key metric in social network analysis
[Freeman ’77, Goh ’02, Newman ’03, Brandes ’03]
• : Number of shortest paths between vertices s and t
• : Number of shortest paths between vertices s and t
passing through v
( )
( )st
s v t V st
v
BC v
s
s¹ ¹ Î
= å
)(vsts
sts
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22. Mining Twitter for Social Good
ICPP 2010
Image credit: bioethicsinstitute.org
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23. Conclusions
• Graph Data Science is an important technique for
solving real-world grand challenges
• Graphs are a natural abstraction for Big Data and
connect people, places, and things
• Graphs are useful in problems such as Data Tagging,
Triage, Exploratory Data Analysis, Anomaly Detection,
Finding Patterns, Insider Threats, Fraud Detection, and
Advanced Analytics
• Graph technologies such as Neo4j provide Enterprise-
class performance
• Getting started with Graph Databases is easy
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24. Graph500 Benchmark, www.graph500.org
• Cybersecurity
• 15 Billion Log Entries/Day (for large
enterprises)
• Full Data Scan with End-to-End Join
Required
• Medical Informatics
• 50M patient records, 20-200
records/patient, billions of individuals
• Entity Resolution Important
• Social Networks
• Example, Facebook, Twitter
• Nearly Unbounded Dataset Size
• Data Enrichment
• Easily PB of data
• Example: Maritime Domain Awareness
• Hundreds of Millions of Transponders
• Tens of Thousands of Cargo Ships
• Tens of Millions of Pieces of Bulk Cargo
• May involve additional data (images,
etc.)
• Symbolic Networks
• Example, the Human Brain
• 25B Neurons
• 7,000+ Connections/Neuron
Defining a new set of benchmarks to guide the design of hardware architectures and
software systems intended to support such applications and to help procurements.
Graph algorithms are a core part of many analytics workloads.
Executive Committee: D.A. Bader, R. Murphy, M. Snir, A. Lumsdaine
• Five Business Area Data Sets:
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