CINET
Team

•  Virginia
Tech:
Keith
Bisset,
Abhijin
Adiga,

Edward
Fox,

Maleq
Khan,
Chris
Kuhlman,
Henning
Mortveit,
Madhav

Marathe,
Samarth
Swarup,
Anil
VullikanB

•  Indiana
University:
Geoﬀ
Fox,
Judy
Qiu,
Stephen
Wu

•  SUNY
Albany:
S.S.
Ravi

•  Jackson
State
University:
Richard
Aló,
Chris
Cassidy

•  University
of
Houston
Downtown:
Ongard
Sirisaengtaksin

•  Argonne
NaBonal

Lab
and
U.
Chicago:
Pete
Beckman

•  VT
Students:
S.M.
Shamimul
Hasan,
Md
Hasanuzzaman,
S
M

Arifuzzaman,
Maksudul
Alam,
Sherif
Abdelhamid,
Zalia

Shams,
Tirtha
Bhaaacharjee

•  Persistent
Systems:
Harsha,
Gaurav,
Tanmay,
Rakhi,

Abhijeet,
Niranjan
and
Team

CINET:
Team
(cont.)

•  Several
evaluators
are
incorporaBng
CINET
into

courses

–  S.
S.
Ravi
at
the
University
at
Albany,
SUNY

–  Edward
Fox
at
Virginia
Tech

–  Anil
VullikanB
at
Virginia
Tech

–  Henning
Mortveit
at
Virginia
Tech

–  Aravind
Srinivasan
at
University
of
Maryland

–  Albert
Esterline
(NCAT)

•  Other
evaluators
planning
to
use
CINET
in

research

–  Zsuzsanna
Fagyal
at
UIUC

–  Maa
Macauley
at
Clemson
University

–  T.
M.
Murali
at
Virginia
Tech

Network

“Network
is
a
group
or
system
of

interconnected
people
or
things”

-‐
Oxford
DicBonaries

“Network
science
is
the
study
of

network
representaBons
of

physical,
biological,
and
social

phenomena”

-‐
NaBonal
Research
Council

Network
Science

•  Research
in
network
science
has
been
increasing

very
rapidly
in
the
last
decade,
in
many
different

scienBfic
fields.

•  Networks
can
be
very
large:
~108
nodes,
~1010

edges,
requiring
HPC
for
analysis

•  There
is
a
need
for
middleware,
i.e.,
an
interface

layer

o  Domain
experts
don’t
need
to
become
experts
in
graph
theory,
data

mining,
and
high-‐performance
compuBng

o  Provides
an
abstracBon
layer
that
allows
separaBon
of
innovaBon

above
and
below
this
layer

CINET:
Vision

•  Self-‐sustainable

–  Users
can
contribute
new
networks,
data,
algorithms,
hardware,
and

research
results

•  Self-‐manageable

–  End
users
will
be
insulated
from
the
complexiBes
of
resource
allocaBon,

scheduling,
cross-‐plahorm
interacBons,
and
other
low-‐level
concerns

•  Repeatable
Science

–  The
exact
version
of
a
model
that
produced
a
result
is
kept

–  All
model
input
parameters
are
captured

–  Any
system
conﬁguraBon
informaBon
is
captured

–  All
input
data
versions
are
kept

–  The
enBre
set
of
conﬁguraBon
informaBon
for
an
experiment
(mulBple

runs)
should
be
accessible
by
providing
a
URL

–  Encourage
users
of
the
system
to
include
pointers
to
results
in
published

work

•  Provides
over
150+
networks,
18
graph
generators
and
80+

measures

•  New
improved
UI
for
Granite

•  Components
(apps)
that
allow
researchers
to
interact
with
CINET:

VisualizaBon
of
networks,
Adding
networks,
Adding
structural

analysis
tools

•  Structural
analysis
using
Galib,
NetworkX
and
SNAP

•  Version
1.0
of
a
Python-‐based
DSL

for
compuBng
complex

workﬂows

•  Resource
manager
1.0
completed:
allows
mulBple
computaBonal

and
analyBcal
resources
to
be
used
and
selected

•  Website
with
addiBonal
resources
(course
notes,
etc.).

Version
2.0

Digital
Library

Digital
Library:

v Support
network
science
research

v Manage
conBnuously
produced,
large-‐scale

scienBfic
output

v Provide
simulaBon-‐specific
services
to
support

science

v Manage
large
network
graphs
and
workflow
of

content
collecBons

Digital
Library

Data:

–  List
of
networks
&
metadata.

–  List
of
measures
&
metadata.

–  Parameters
for
measures.

–  List
of
generators
&
metadata.

–  Parameters
for
generators.

Services:

— MemoizaBon:
Record
details
of
every
experiment
run

— IncenBvizaBon:
Report
how
many
Bmes
a
parBcular

graph
was
used

— Browsing
and
Searching:
graphs,
measures,
results

TransacBonal
Data

•  Following
data
is
stored
in
database

–  Users

–  Details
Network
Analysis
run
by
users
including
parameters
set
for

each

–  Details
Generator
Analysis
run
by
users
including
parameters
set
for

each

•  Following
is
stored
in
file
system

–  Output
files
of
Network
&
Generator
Analysis.

•  Mapping
exists
between
data
stored
in

database
and
file
system

Performance
Improvements

•  Blackboard
is
used
ONLY
for
placing
job

request

•  Simpler
&
fewer
number
of
components

•  Components
are
fully
distributed
–
Web-‐app,

blackboard,
brokers
exist
on
separate
VMs

•  Brokers
are
no
more
required
to
poll
the
data

but
directly
noBﬁed
by
blackboard
container.

Resource
Manager

•  Decides
what
is
the
best
resource
for
a
given

job
request

– Through
a
set
of
deﬁned
rules

•  Tracks
the
health
of
and
load
on
compute

resources

– And,
considers
this
knowledge
in

determining
the
best
resource(s)

Granite

Structural
Analysis
of
Complex

Networks

Graph
Analysis
Resources
and
Challenges

•  Resources
:

–  StaBc
Analysis
tools:
Provide
eﬃcient
implementaBons
of

various
graph
measures
or
algorithms
(e.g.,
Galib,

NetworkX).

–  Large
collecBon
of
Data
Sets
(of
networks)

•  Challenge
1:
How
can
we
make
an
analyBc
engine
that
will

–  Reduce
programming
overhead,

–  Reuse

exisBng
resources

•  Challenge
2:
Provide
a
simple
computaBonal
interface
to

Domain
Experts
to
use
available
resources
and
program

interacBvely

CINET
-‐
Granite

•  Granite
allows
users
to
run
various
network
measures
on
a
variety

of
networks

–  Measures
can
either
be
staBc
(e.g.,
degree
distribuBon,
cluster

coefficient)
or
dynamic
(e.g.,
disease
diffusion)

–  Network
size
can
range
from
Bny
(10s
of
nodes)
to
very
large

(100s
of
millions
of
nodes)

•  Granite
automaBcally
picks
best
implementaBon
of
specified

measure

•  Granite
automaBcally
picks
most
appropriate
compute
resource

•  Granite
includes
modules
from
three
graph
algorithm

libraries:

–  Galib
(developed
at
NDSSL)

–  NetworkX
(developed
at
Los
Alamos
NaBonal
Lab)

–  SNAP
(developed
at
Stanford
University)

Graph
Libraries

CINET:
A
CyberInfrastructure
for

Network

Science

Graph
Centrality
Measures
in
CINET

u  Degree
list
<Node-‐ID,
Degree>

u  Degree
statistics

u  Degree
distribution

u  Average
neighbor
degree

u  Hub-‐authority

u  Pagerank

u  Clustering
coeﬃcient
distribution

u  Streaming-‐based
CC
distribution
(apprx.)

u  Betweenness
centrality

u  Closeness
centrality

u  Degree
centrality

u  Eigenvalue
centrality

u  k-‐core

u  k-‐crust

u  k-‐corona

u  k-‐clique
coeﬃcient

u  Core
number

u  Ro
distribution

u  Coreness
of
nodes
<ID,
coreness>

u  CC
list

<Node-‐ID,
CC>

u  External-‐memory
CC
algorithm

(exact)

u  Parallel
CC
algorithm

u  Generate
degree
sequence

u  Closeness
centrality
-‐
weighted

u  Ro
distribution

u  Closeness
vitality
–

unweighted

u  Closeness
vitality
-‐
weighted

u  Communicability
centrality

u  In-‐degree
centrality

u  Out-‐degree
centrality

Graph
Shortest
path
and

ConnecBvity
Measures
in
CINET

u  Number
of
connected
components

u  Component
graph

u  Component
size
distribution

u  Strongly
connected
component

u  Weakly
connected
component

u  Bi-‐connected
component

u  Check
bi-‐connectivity

u  BFS
tree
/
forest

u  BFS
predecessor
list

u  BFS
successor
list

u  Partitioning
by
BFS
traversal

u  DFS
predecessor
list

u  DFS
Successor
list

u  DFS:
nodes
in
post-‐order

visits

u  DFS
Tree

u  Articulation
point

u  Bridge
edges

u  Diameter

u  Center

u  Periphery

u  Check
connectivity
u  Eccentricity

u  Radius

u  DFS:
nodes
in
pre-‐order
visits
u  Check
if
graph
is
s
DAG

u  Topological
sort

Weighted
Shortest
Path
and
MoBf
counBng

u  Minimum
spanning
tree

u  Single
source
shortest
path

Weighted
shortest
path
related

u  Shortest
path
tree/forest

u  Weighted
diameter
(exact
and
approx.)

u  Average
pairwise
distance
(exact
and
approx.)

u  Distribution
of
pair-‐wise
distance
(exact
and
approx.)

Subgraph
/
Motif
counting

u  Count
triangle

u  Clique
counts
(specialized)

u  Graph
transitivity

u  All
maximal
clique

u  Clique
number

u  Largest
clique
containing
a
node

Flow

u  Maximum
ﬂow

u  Minimum
cut

CINET:
A
CyberInfrastructure
for

Network

Science

Other
Measures

u  Shuffle
edges

u  Degree-‐assortative
shuffle

u  Age-‐assortative
shuffle

u  Compare
graphs

u  Remove
nodes

u  Remove
edges

u  Remove
high
degree
nodes

(top
x%)

u  Remove
high
degree
nodes
(degree
>=x)

u  Check
if
a
degree
sequence
is

graphical

u  Compare
graphs

u  Isolated
nodes

u  Vertex
cover

u  Dominating
set

u  Minimum
edge
dominating
set

u  Check
graph
consistency

u  Check
if
bipartite
graph

u  Check
if
chordal
graph

u  Maximal
independent
set

u  Number
of
common
neighbors

CINET:
A
CyberInfrastructure
for

Network

Science

Simple
GeneraBve
Models
of

Networks
in
CINET

u Random
graph
generators

u  Erdos-‐Renyi
random
graph

u  G(n,
p)
graph

u  G(n,
p)
component

u  G(n,
m)
graph

u  G(n,
r)
graph

u  Watts-‐Strogatz
small-‐world
graph

u  Waxman
random
graph

u  Chung-‐Lu

u  Havel-‐Hakimi

u  Preferential
Attachment

u  Small
world

u  Circle

u  Star

u  Chain

u  Lattice

u Deterministic
graph

generators

u  Binary
tree
graph

u  Star

u  Wheel

u  Grid

u  Torus

u  Hypercube

u  Petersen

Currently
Available
Networks

•  150+
small
and
large
networks

–  Sizes
vary
from
100
edges
to
110M
edges

–  Social
contact
networks

•  Chicago,
Washington
DC,
Detroit,
New
York,
Seattle

–  Multi-‐modal
urban
transportation
networks
(e.g.,
subway,
cars,

buses).

•  Portland,
OR

–  Adolescent
friendship
networks

•  High
school
in
New
River
Valley

–  Blog
and
other
online
networks

•  Slashdot,
Epinions

–  Infrastructure
networks

•  Ad
hoc
and
mesh,
phone
call,
electrical
power

–  Biological
networks

Networks
in
CINET
(cont.)

Types
of
Networks

u  Web
graph

u  Autonomous
System/Internet

u  Road/transport
networks

u  Collaboration
networks

u  Co-‐appearance
networks

u  Social
networks

u  Biological
networks

u  Infrastructure(e.g.
power)

u  Others

u  Stanford
SNAP

u  Pajek
Dataset

u  http://www-‐personal.umich.edu/~mejn/netdata/

u  Some
others
publicly
available
sources

Original
Sources

List
of
Networks

Autonomous
System/Internet
Web
Graph

u  Autonomous
systems
-‐
Oregon-‐1
-‐
010331

u  Autonomous
systems
-‐
Oregon-‐1
-‐
010407

u  Autonomous
systems
-‐
Oregon-‐1
-‐
010414

u  Autonomous
systems
-‐
Oregon-‐1
-‐
010421

u  Autonomous
systems
-‐
Oregon-‐1
-‐
010428

u  Autonomous
systems
-‐
Oregon-‐1
-‐
010505

u  Autonomous
systems
-‐
Oregon-‐1
-‐
010512

u  Autonomous
systems
-‐
Oregon-‐1
-‐
010519

u  Autonomous
systems
-‐
Oregon-‐1
-‐
010526

u  Autonomous
systems
-‐
Oregon-‐2
-‐
010331

u  Autonomous
systems
-‐
Oregon-‐2
-‐
010407

u  Autonomous
systems
-‐
Oregon-‐2
-‐
010414

u  Autonomous
systems
-‐
Oregon-‐2
-‐
010421

u  Autonomous
systems
-‐
Oregon-‐2
-‐
010428

u  Autonomous
systems
-‐
Oregon-‐2
-‐
010505

u  Autonomous
systems
-‐
Oregon-‐2
-‐
010512

u  Autonomous
systems
-‐
Oregon-‐2
-‐
010519

u  Autonomous
systems
-‐
Oregon-‐2
-‐
010526

u  The
Internet
Topology
Zoo
-‐
AboveNet

u  The
Internet
Topology
Zoo
-‐
AGIS

u California
Web
Graph

u EPA
Web
Graph

u EuroSiS
web
mapping
study

u Web
Graph
of
Berkeley
and

Stanford

Collaboration
Graph

u Condense
Matter
collaboration
network

u Condensed
Matter
collaborations
1999

u Condensed
Matter
collaborations
2003

u Condensed
Matter
collaborations
2005

u CS
PhD
supervision
relation
graph

u Erdos
Collaboration
Network

u General
Relativity
and
Quantum
Cosmology

collaboration
network

u High-‐Energy
Theory
Collaboration
Network
2001

u High-‐Energy
Theory
Collaboration
network
2003

u Network
Science
Collaboration

u Phenomenology
Collaboration
Network

Social,
Proximity
and

Infrastructure

Networks

u Miami
Chung-‐Lu

u Miami
Contact
Network

u Portland
Contact
Network

u Primary
School
Cumulative

Networks
1

u Primary
School
Cumulative

Networks
2

u Seattle
Contact
Network

u Slashdot
Social
Network
2008

u Slashdot
Social
Network
2009

u Youtube
Social
Network

Road/Transport/Infrastructure

Networks

u Airlines

u California
transportation
Network

u Pennsylvania
transportation

network

u Texas
transportation
network

u US
Air
Lines

u US
Power
Grid

u Western
States
Power
Grid

u Dolphins'
Social
Network
in
NZ

u Brightkite
Friendship
network

u Enron
Email
Data
with
Manager-‐Subordinate

Relationship
Metadata

u Enron
email
Network

u Enron
Giant
Component

u Epinions
Scoical
Network

u Giant
Component
of
Brightkite
Network

u Giant
Component
of
Epinions
Networks

u Giant
Component
of
Gowalla
Network

u Giant
Component
of
Max
Planck's
Facebook

Network

u Giant
Component
of
Slashdot0811
Network

u Giant
Component
of
Slashdot0902
Network

u Gowalla
friendship
network

u Hypertext
2009
dynamic
contact
network

u Hyves
Social
Network

u Infectious
SocioPatterns
-‐
2009-‐04-‐28

u Infectious
SocioPatterns
-‐
2009-‐04-‐29

u Karate
network

u LiveJournal
Social
Network

u Max
Planck
-‐
Flickr
Social
Network

List
of
Networks
(Contd.)

Biological
Networks

Co-‐appearance/co-‐purchase

Networks

•  C.
Elegans
Neural
Network

•  Yeast
PPI
network

Games/Sports
Networks

•  American
College
Football

Network

•  Soccer
WorldCup'98

•  Les
Miserables

•  Network
Gloassary

•  PoliBcs
books

•  Word
adjacencies

Others/misc.
Networks

•  Dynamic
Java
code

•  Small
World
Network

Making
Granite
Self-‐Sustainable:

Concept
of
Services
and
Apps

User
Management

•  User
can
request
account.
Account
is
operaBonal

only
aser
Admin
acBvates
it.

•  Admin
can
acBvate
or
deacBvate
accounts.

•  User
can
change
password.

•  All
the
enBBes
–
Networks,
Measures,
Generators,

Analyses
–
have
owners.

Add
Network

•  User
can
add
network
by
uploading
network
ﬁle

•  Uploaded
network
is
validated

•  For
valid
networks,
edges
&
nodes
are
automaBcally

calculated

•  Networks
are
converted
into
.gph
&
.nx
format
–

•  User
can
specify
metadata
for
the
uploaded
network

•  User
can
specify
if
the
network
is
–

–  Public
:
available
to
all
users
for
analysis.

–  Private:
available
to
only
the
owner,
which
is
the
default

opBon

VisualizaBon

•  CINETViz
app
fully
integrated
in
Granite.

•  User
can
submit
visualizaBon
job
for
a
network.

•  VisualizaBon
process
is
scalable
&
abstracted

from
backend
through
middleware
(blackboard
&

brokers)

•  Once
visualizaBon
job
is
completed,
user
can

view
&
download
generated
visualizaBon.

•  VisualizaBon
has
2
user
interfaces
in
Granite

–  Quick
view
while
selecBng
network
for
analysis

–  Detailed
view
in
VisualizaBon
tab

Features
–
VisualizaBon

VisualizaBon
of
Networks
(Contd.)

Karate Club NetworkMiami Graph

VisualizaBon
of
Networks
(Contd.)

Amazon Co-purchase Network

CINET
website

•  Central
locaBon
of
CINET

•  Portal
for
course
materials

•  Web
address

hJp://www.vbi.vt.edu/ndssl/cinet

CINET:
A
CyberInfrastructure
for

Network

Science

Graph
Dynamical
Systems
Calculator
(GDSC)

•  Provide
a
Web
ApplicaBon
to

enable
users
to
compute

dynamics
for
their
systems.

•  Evaluate
arbitrary
(small)

graphs,
a
range
of
vertex

funcBons,
and
update

schemes.

•  GDSC
is
an
applicaBon
in

CINET.

Overview

Future
Work

•  Add
graph
modificaBon
algorithms

–  Remove
edges

–  Swap
edges

•  Add
data
model
to
manage
system
workflow

•  Domain
specific
language

•  Registry
Service

Digital
Library
to
support

ComputaBonal
Epidemiology
Datasets

SyntheBc
InformaBon
Based
Epidemiological

Laboratory
(SIBEL)

The
Problem

•  ComputaBonal
epidemiology
employs
computer

models
and
informaBcs
tools
to
reason
about
the

spaBo-‐temporal
spread
of
diseases.

•  Studies
are
conducted,
in
general,
through
the

use
of
a
simulaBon
and
require
informaBon
on

the
populaBon
structure,
agent
behavior,
disease

transmission,
and
a
model
of
the
disease.

•  The
heterogeneous
content
includes
metadata,

text,
tables,
spreadsheets,
experimental

descripBons,
and
large
result
ﬁles.

NDSSL’s
networked
epidemiology
data
repository

Category
Data
Size
Representation

Synthetic

Population

Household,

Person

Activity

566
GB
Relational

Social

Network

and
Output

Contact

Network,

Simulation

Output

1.84
TB
File

Experiment
Experiment
240
GB
Relational

The
Problem
(cont.)

•  Data
access
and
digital
library
services
in
current
setups
are

cumbersome
due
to
heterogeneity
and
fragmentaBon

across
datasets.

•  There
is
no
accepted
framework
that
allows
unified
access

to
such
content.

•  The
diversity
of
models,
data
sources,
data

representaBons,
and
modaliBes
that
are
collected,
used,

and
modified
moBvate
the
development
of
a
digital
library

(DL)
framework
to
support
computaBonal
epidemiology.

•  We
propose
a
data
mapping
framework
for
digital
library

systems
for
computaBonal
epidemiology
datasets.

•  The
proposed
framework
provides
a
unified
view
to
access

and
query
complete
epidemiology
workflow
data.

Uniﬁed
View
to
Access
and
Query
Complete

Epidemiology
Workﬂow
Data

Resource
DescripBon
Framework
(RDF)

•  Directed
labeled
graphs

•  Model
elements

–  Resource:
These
are
the
things
being
described
by

RDF
expressions.

–  Property:
Is
a
speciﬁc
aspect,
characterisBc,
aaribute

or

relaBon
used
to
describe
a
resource
Value

–  Statement:
A
statement
in
RDF
consists
of

resource
+
property
+
value

subject

predicate

object

RDF
Example

•  For
the
statement
“Shamimul
Hasan
is
the
creator
of
the

web
page
www.vt.edu/~shasan2.

•  We
have
RDF
statement
as

•  Node
and
arc
diagram
as

Subject(resource)
www.vt.edu/~shasan2

Predicate(property)
creator

Object(literal)

“Shamimul
Hasan”

www.umr.edu/~shasan2 Shamimul Hasan
creator

Framework

•  Data
mapping
provides
us
the
ﬂexibility
to
switch
between
various

databases
and
execute
queries
on
them.

Experimental
Study

•  We
considered
a
real-‐Bme
epidemiology

simulaBon
study
conducted
in
the
Seaale

area.
The
study
assumed
that
inﬂuenza

transmits
in
various
regional
populaBons

through
person-‐person
contact.

•  We
use
the
D2RQ
Mapping
Language
to

convert
relaBonal
and
ﬁle
data
to
RDF
graphs,

Virtuoso
Open-‐Source
EdiBon
6.1.6
as
RDF

data
engine,
and
the
SPARQL
query
language.

Experimental
Study
(cont.)

Databases
RDF

Graph
Size
(GB)

Number

of
Triples

RDF
Graph

Generation

Time
(Minutes)

Seattle

Synthetic

Population

177
661,848,662
317

Output
3.10
12,979,996
6

Experiment
0.01
66,654
0.37

Experimental
Study
(cont.)

Queries
Bottom-‐up
Approach

(SPARQL
Query

Runtime
in
Seconds)

Top-‐down
Approach

(SPARQL
Query

Runtime
in
Seconds)

How
many
people
of
a

particular
demographic

are
sick?

0.04
7.18

Find
who
infected

whom

of
a
particular

Demographic

0.38
9.18

How
many
people
get

infected
on
a
particular

simulation
day?

0.03
5.76

Reference

•  Sherif
Hanie
El
Meligy
Abdelhamid,
Md.
Maksudul
Alam,
Richard
Aló,
Shaikh
Arifuzzaman,
Peter
H.

Beckman,
Tirtha
Bhaaacharjee,
Md
Hasanuzzaman
Bhuiyan,
Keith
R.
Bisset,
Stephen
Eubank,

Albert
C.
Esterline,
Edward
A.
Fox,
Geoﬀrey
Fox,
S.
M.
Shamimul
Hasan,
Harshal
Hayatnagarkar,

Maleq
Khan,
Chris
J.
Kuhlman,
Madhav
V.
Marathe,
Natarajan
Meghanathan,
Henning
S.
Mortveit,

Judy
Qiu,
S.
S.
Ravi,
Zalia
Shams,
Ongard
Sirisaengtaksin,
Samarth
Swarup,
Anil
Kumar
S.
VullikanB,

Tak-‐Lon
Wu:
CINET
2.0:
A
CyberInfrastructure
for
Network
Science.
eScience
2014:
324-‐331

•  S.
M.
Shamimul
Hasan,
Sandeep
Gupta,
Edward
A.
Fox,
Keith
R.
Bisset,
Madhav
V.
Marathe:
Data

mapping
framework
in
a
digital
library
with
computaBonal
epidemiology
datasets.
JCDL
2014:

449-‐450

•  S.
M.
Shamimul
Hasan,
Keith
R.
Bisset,
Edward
A.
Fox,
Kevin
Hall,
Jonathan
Leidig,
Madhav
V.

Marathe:
An
Extensible
Digital
Library
Service
to
Support
Network
Science.
ICCS
2013:
419-‐428

•  Sherif
Elmeligy
Abdelhamid,
Richard
Aló,
S.
M.
Arifuzzaman,
Peter
H.
Beckman,
Md
Hasanuzzaman

Bhuiyan,
Keith
R.
Bisset,
Edward
A.
Fox,
Geoﬀrey
Charles
Fox,
Kevin
Hall,
S.
M.
Shamimul
Hasan,

Anurodh
Joshi,
Maleq
Khan,
Chris
J.
Kuhlman,
Spencer
J.
Lee,
Jonathan
Leidig,
Hemanth

MakkapaB,
Madhav
V.
Marathe,
Henning
S.
Mortveit,
Judy
Qiu,
S.
S.
Ravi,
Zalia
Shams,
Ongard

Sirisaengtaksin,
Rajesh
Subbiah,
Samarth
Swarup,
Nick
Trebon,
Anil
VullikanB,
Zhao
Zhao:

•  CINET:
A
cyberinfrastructure
for
network
science.
eScience
2012:
1-‐8

•  Resource
DescripBon
Framework
(RDF)
developed
by

World
Wide
Web
ConsorBum
(W3C)-‐
hap://
bit.ly/1aXP5k2

Student
AcBvity

•  Please
Visit
Granite
website:

hap://ndssl.vbi.vt.edu/apps/cinet/

•  Launch
App

•  Login

–  Username:
demo

–  Password:
demo1234

•  Start
a
New
Analysis
with
“Karate”
network

and
“PageRank”
measure.

•  Check
analysis
report.

Extensible
MemoizaBon
Service

•  Query
a
set
of
digital
objects
that
exactly
match
a
metadata

paaern

•  UBlizaBon

–  EducaBon
–
students

–  Baseline
scenarios

–  Comparisons,
body
base,
similar
regions

•  Small
|G|
<
100,000

–  Example:
RND-‐G(n,p)
Random
Graph
1
(nodes:1,000,

edges:
4,971)

•  Medium
100,000
≤|G|<10,000,000

–  Example:
RND-‐G(n,p)
Random
Graph
500
(nodes:

500,000,
edges:
5.00E+06)

•  Large
|G|≥10,000,000

–  Example:
Seaale
contact
network
(nodes:
3,207,037,
and

edges:
8.66E+07).

Network
Category

Performance

§  Shadowfax
(Virginia
Tech)

§  912
cores,
5
TB
RAM,
80
TB
storage,
7168
CUDA
cores

§  100+
networks

§  100+
measures

CINET: A CyberInfrastructure for Network Science

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