A Glimpse of CINET Workings & Purpose as part of the CINET Workshop on July 10th, 2015 in Blacksburg, VA.

1. CINET:
A
Cyber-­‐Infrastructure
for
Network
Science
(Overview)
NSF
Software
Development
for
CyberInfrastructure
Grant
OCI-­‐1032677
Additional
support
by
grants
from
DTRA
V&V,
DTRA
CNIMS,
NSF
NetSE,
NSF
DIBBS
Team
Virginia
Tech,
Indiana
U.,
SUNY
Albany,
Jackson
State,
Argonne
Na>onal
Lab,
U.
Chicago,
NCAT,
U.
Houston
Downtown

2. Goal:
A
Glimpse
of
CINET
Workings
&
Purpose
• Workings
– Workshop:
hands-­‐on
use
&
demonstraHons.
– Worthwhile:
high
level
• Glimpse
of
CINET
“insides.”
• AppreciaHon
for
what
goes
on
behind
the
UIs.
• CINET
– A
community
resource.
2

3. 0"
1000000"
2000000"
3000000"
4000000"
5000000"
6000000"
7000000"
2000" 2002" 2004" 2006" 2008" 2010"
Network
Science
• Research
in
network
science
has
been
increasing
very
rapidly
in
the
last
decade,
in
many
different
scienHfic
fields.
• Several
conferences
and
journals;
e.g.,
ASONAM,
WWW,
Web
Sci,
Network
Science.
• Networks
can
be
very
large:
~108
nodes,
~1010
edges,
requiring
HPC
for
analysis
• There
is
a
need
for
middleware,
i.e.,
an
interface
layer
– Domain
experts
do
not
need
to
become
experts
in
graph
theory,
data
mining,
and
high-­‐performance
compuHng
Number of papers with
“Complex Networks” in the
title
“Network
science
is
the
study
of
network
representations
of
physical,
biological,
and
social
phenomena”
3
MAU=monthly
acHve
users
The Motley Fool

4. Network
Science
4
How
many
connecHons
does
the
person
in
orange
have?
Who
are
the
mostly
highly
connected
people?
How
many
connected
groups
are
in
a
populaHon?
How
many
“friends-­‐of-­‐friends”
arrangements
are
there?
Who
are
the
people
(computers,
etc.)
that
are
on
the
most
pathways
between
other
pairs
of
agents?
If
I
“seed”
(infect)
the
orange
person,
how
does
the
infecHon
spread?
network
IllustraHve
quesHons

5. CINET
To
A
User
user
user
Networks

6. CINET
To
A
User
user
user
●
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100
101
102
103
104
105
100
101
102
103
Degree
NumberofNodes
4B
node
graph
generator
Networks
Network
generators
and
measures
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
FractionofNodes
Cluster Coefficient
Cluster Coefficient Distribution-Miami
No Shuffle
10% Shuffle
50% Shuffle
100% Shuffle
Miami

7. CINET
To
A
User
7
user
user
●
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100
101
102
103
104
105
100
101
102
103
Degree
NumberofNodes
4B
node
graph
generator
0"
0.001"
0.002"
0.003"
Base"
0+10"
11+20"
21+30"
31+40"
41+50"
51+60"
61+70"
71+80"
81+90"
Frac%of%Popula,on%
Age%Range%for%Vaccina,on%
Liberia
Mexico
City
Networks
Network
generators
and
measures
Network
dynamics
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
FractionofNodes
Cluster Coefficient
Cluster Coefficient Distribution-Miami
No Shuffle
10% Shuffle
50% Shuffle
100% Shuffle
Miami

8. CINET
Underneath
8
user
user
Client/server

9. CINET
Underneath
9
user
user
Parallel
Distributed
Algorithms
1.
counHng
triangles.
2.
edge
swapping.
3.
converHng
graph
formats.
4.
simulaHon.
5.
…
others
…
Client/server

10. CINET
Underneath
10
user
user
Parallel
Distributed
Algorithms
1.
counHng
triangles.
2.
edge
swapping.
3.
converHng
graph
formats.
4.
simulaHon.
5.
…
others
…
Input
Checking:
1.
immediate
value.
2.
values
within
a
screen.
3.
values
across
screens.
Client/server

11. CINET
Underneath
11
●
●
●
●
●
0
50
100
150
2010 2011 2012 2013 2014
Year
Numbers
● Modules
Networks
user
user
Parallel
Distributed
Algorithms
1.
counHng
triangles.
2.
edge
swapping.
3.
converHng
graph
formats.
4.
simulaHon.
5.
…
others
…
Input
Checking:
1.
immediate
value.
2.
values
within
a
screen.
3.
values
across
screens.
Client/server

12. CINET—What
Is
It?
• Cyber-­‐infrastructure
for
network
science.
• Suite
of
applicaHons
– Granite:
network
structure;
measures,
graphs.
– EDISON:
network
dynamics;
models.
– GDSC:
network
dynamics
(full);
models.
– Organic
expansion.
• SupporHng
services
• Infrastructure
• Environment
for
collaboraHve
science.
• Community
resource.
12

13. Community
Resource
13
CINET
networks
algorithms
simulaHons
resources
annotaHons
course
materials
analyses
Community
member
contribuHons

14. CINET
Layered
Architecture
VizApp:
App
for
network
visualization
Granite:
Graph
structural
analysis
GDSC:
Phase
space
analysis
of
graph
dynamics
Computing
resources
and
data
storage
Simfrastructure
Case
studies
Add
network
Add
structural
method
Store
results
Add
data
and
statistical
analysis
method
14
EDISON:
Network
dynamics;
spread
of
contagions
over
networks
Research
Uses
Tools
in
CINET
Middleware/Workflow
Hardware
Metadata
Curation
Memoization
Incentivization
DL/Common
Services
Networks
(directed
attributed)
Services
for
network
manipulation
Netscript
Network
science
courses
(Albany,
NCAT,
JSU,
VT)

15. CINET
Layered
Architecture
VizApp:
App
for
network
visualization
Granite:
Graph
structural
analysis
GDSC:
Phase
space
analysis
of
graph
dynamics
Computing
resources
and
data
storage
Network
science
courses
(Albany,
JSU,
NCAT,
VT)
Case
studies
Add
structural
method
Store
results
Add
data
and
statistical
analysis
method
15
EDISON:
Network
dynamics;
spread
of
contagions
over
networks
Research
Uses
Tools
in
CINET
Hardware
DL/Common
Services
Networks
(directed
attributed)
Services
for
network
manipulation
UI UI UI
Simfrastructure
Middleware/Workflow
Netscript
Under
the
hood
Add
network
Metadata
Curation
Memoization
Incentivization

16. • Structural
Analysis
Tool
(Granite)
– 110+
networks
(graphs)
– 18+
network
generators
– 70+
network
algorithms
(measures);
GaLib,
SNAP
(Stanford),
NetworkX
– VisualizaHon
of
networks;
Gephi
– Service
for
adding
new
networks
(graphs)
– Service
for
adding
new
structural
analysis
tools
(graph
algorithms)
• Graph
Dynamical
System
Calculator
(GDSC)
– Analyzing
the
phase
structure
of
GDS;
small
graphs
– 13
graph
templates;
15
vertex
funcHon
(behavior)
families.
• SimulaHon
of
Dynamics
(EDISON)
– Compute
(contagion)
dynamics
on
larger
networks:
simulaHon.
– Services
to
manipulate
a"ributed
networks
and
to
run
simulaHons.
– Several
contagion
models;
with
and
without
intervenHons.
CINET
Apps
Overview

17. • Structural
Analysis
Tool
(Granite)
– 110+
networks
(graphs)
– 18+
network
generators
– 70+
network
algorithms
(measures);
GaLib,
SNAP
(Stanford),
NetworkX
– VisualizaHon
of
networks;
Gephi
– Service
for
adding
new
networks
(graphs)
– Service
for
adding
new
structural
analysis
tools
(graph
algorithms)
• Graph
Dynamical
System
Calculator
(GDSC)
– Analyzing
the
phase
structure
of
GDS;
small
graphs
– 13
graph
templates;
15
vertex
funcHon
(behavior)
families.
• SimulaHon
of
Dynamics
(EDISON)
– Compute
(contagion)
dynamics
on
larger
networks:
simulaHon.
– Services
to
manipulate
a"ributed
networks
and
to
run
simulaHons.
– Several
contagion
models;
with
and
without
intervenHons.
CINET
Apps
Overview
StaHcs/Structure
Dynamics

18. • Middleware
– Sending
messages
(requests
for
services,
status);
sending
data.
– Brokers
for
services
provide
communicaHon
with
services.
• Resource
Manager
– Allows
mulHple
computaHonal
resources
to
be
used
and
selected.
– Uses
remote
grids,
clouds.
• Netscript
– Workflows.
• Digital
Library
(DL)
– Metadata/data
storage,
organizaHon.
– OperaHons:
curaHon,
memoizaHon,
incenHvzaHon,
etc.
• (Common)
Services
– Support
and/or
interact
with
DL,
web
apps.
– Example:
Query
services,
data
assignment
service.
• Website
– AddiHonal
resources
(course
notes,
videos,
tutorials,
research
papers
etc).
CINET
Infrastructure
Overview

19. CINET
User
Benefits
19
correctness
reproducibility
reuse
security
Open
access
system
customizaHon
privacy
models
applicaHons
algorithms

20. Selected
Challenges
• Challenge
1:
Simple
computaHonal
interface
for
domain
experts
with
linle
training.
– (ComputaHonal
experts,
too)
• Challenge
2:
Large
networks.
• Challenge
3:
Data
management
and
movement.
20

21. Types
of
PublicaHons
• System
(architecture)
• Algorithms
• Dynamical
systems
characterizaHons
• Uses
(applicaHons)
21

22. PublicaHons—Architecture/Use
• CINET
team,
“CINET
2.0:
A
CyberInfrastructure
for
Network
Science,”
eScience
2014.
• CINET
Team,
“CINET:
A
CyberInfrastructure
for
Network
Science,”
eScience
2012.
• Abdelhamid
et.
al.,
“GDSCalc:
A
Web-­‐Based
ApplicaHon
for
EvaluaHng
Discrete
Graph
Dynamical
Systems,”
Plos
One
2015.
22

23. PublicaHons—Algorithms
• Kuhlman
et.
al.,
“A
General-­‐Purpose
Graph
Dynamical
System
Modeling
Framework,”
WSC
2011.
• Maksudul
Alam
and
Maleq
Khan,Parallel
Algorithms
for
GeneraHng
Random
Networks
with
Given
Degree
Sequences,
12th
IFIP
Interna4onal
Conference
on
Network
and
Parallel
Compu4ng
(NPC),
New
York
City,
Sep.
2015.
• Shaikh
Arifuzzaman,
Maleq
Khan
and
Madhav
Marathe,
A
Space-­‐efficient
Parallel
Algorithm
for
CounHng
Exact
Triangles
in
Massive
Networks,
17th
IEEE
Interna4onal
Conference
on
High
Performance
Compu4ng
and
Communica4ons
(HPCC),
New
York
City,
Aug.
2015.
• Shaikh
Arifuzzaman
and
Maleq
Khan,
Fast
Parallel
Conversion
of
Edge
List
to
Adjacency
List
for
Large-­‐Scale
Graphs,
23rd
High
Performance
Compu4ng
Symposium
(HPC),
Alexandria,
VA,
USA,
April
2015.
• Hasanuzzaman
Bhuiyan,
Jiangzhuo
Chen,
Maleq
Khan,
and
Madhav
V.
Marathe,Fast
Parallel
Algorithms
for
Edge-­‐
Switching
to
Achieve
a
Target
Visit
Rate
in
Heterogeneous
Graphs,
Interna4onal
Conference
on
Parallel
Processing
(ICPP),
Minneapolis,
Sep.
2014.
• Maksudul
Alam,
Maleq
Khan,
and
Madhav
V.
Marathe,Distributed-­‐Memory
Parallel
Algorithms
for
GeneraHng
Massive
Scale-­‐free
Networks
Using
PreferenHal
Anachment
Model,
Intl.
Conf.
for
High
Performance
Compu4ng,
Networking,
Storage
and
Analysis
(SuperCompu>ng),
Denver,
Nov.
2013.
• Shaikh
Arifuzzaman,
Maleq
Khan,
and
Madhav
V.
Marathe,PATRIC:
A
Parallel
Algorithm
for
CounHng
Triangles
in
Massive
Networks,
ACM
Conference
on
Informa4on
and
Knowledge
Management
(CIKM),
San
Francisco,
Oct.
2013.
• Zhao
Zhao,
Guanying
Wang,
Ali
Bun,
Maleq
Khan,
V.S.
Anil
Kumar,
and
Madhav
Marathe,
SAHAD:
Subgraph
Analysis
in
Massive
Networks
Using
Hadoop,
26th
IEEE
Interna4onal
Parallel
&
Distributed
Processing
Symposium
(IPDPS),
Shanghai,
China,
May
2012.
• Zhao
Zhao,
Maleq
Khan,
V.S.
Anil
Kumar
and
Madhav
V.
Marathe,
Subgraph
EnumeraHon
in
Large
Social
Contact
Networks
using
Parallel
Color
Coding
and
Streaming,
39th
Interna4onal
Conference
on
Parallel
Processing
(ICPP),
San
Diego,
California,
Sep.
2010.
23

24. PublicaHons—Dynamical
Systems
• Kuhlman,
Chris
J.,
and
Henning
S.
Mortveit,
“Limit
Sets
of
Generalized,
MulH-­‐Threshold
Networks,”
Journal
of
Cellular
Automata,
Vol.
10,
pp.
161-­‐193,
2015.
• Kuhlman,
Chris
J.,
and
Henning
S.
Mortveit,
“Anractor
Stability
in
Nonuniform
Boolean
Networks,”
Theore9cal
Computer
Science,
Vol.
559,
pp.
20-­‐33,
2014.
• Kuhlman,
Chris
J.,
Henning
S.
Mortveit,
David
Murrugarra,
and
V.
S.
Anil
Kumar,
“BifurcaHons
in
Boolean
Networks,”
Automata,
pp.
29-­‐46,
2011.
The
group
has
many
publica>ons
on
dynamical
systems;
these
use
GDSC.

25. PublicaHons—ApplicaHons
• Dumas,
C.,
D.
LaManna,
T.
M.
Harrison,
S.
S.
Ravi.
L.
Hagen,
C.
Kowila
and
F.
Chen,
``Examining
PoliHcal
MobilizaHon
of
Online
CommuniHes
through
E-­‐peHHoning
Behavior
in
We
the
People
(Extended
Abstract),
presented
at
the
Social
Media
and
Society
Conference,
Toronto,
Canada,
Oct.
2014.
• Dumas,
C.,
D.
LaManna,
T.
M.
Harrison,
S.
S.
Ravi.
L.
Hagen,
C.
Kowila
and
F.
Chen,
``Examining
PoliHcal
MobilizaHon
of
Online
CommuniHes
through
E-­‐peHHoning
Behavior
in
We
the
People",
accepted
for
publicaHon
the
Journal
of
Big
Data
and
Society,
2015.
• Dumas,
C.,
D.
LaManna,
T.
M.
Harrison,
S.
S.
Ravi.
L.
Hagen,
C.
Kowila
and
F.
Chen,
``E-­‐peHHoning
as
CollecHve
PoliHcal
AcHon
in
We
the
People",
Proc.
iConference
2015,
Newport
Beach,
CA,
March
2015
(20
pages).

26. CINET
in
Context
• User
interface—all
user
interacHon.
– No
need
to
program.
– No
need
for
HPC
resources.
• Types
of
analysis
– Network
structural
characterizaHons.
– Dynamics
on
networks.
• Large
networks
– GeneraHon.
– Analyses.
• MulHple
tools
provided
under
a
CINET
umbrella.
• Crowd-­‐sourced
plaworm
– Self-­‐sustaining.
– Self-­‐managing.
• CollaboraHve
science.
• Community
resource.
26
There
are
many
good
tools;
but
none
to
our
knowledge
so
widely
encompassing.

27. 27
END