Transcript PSU IDay 2 Apr 2014 -swami.ppt

ST, NETWORK SCIENCE
Computational & Information Sciences
U.S. Army Research Laboratory
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Sept 2006
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Welcome to Network Science Corporation The Web Journal of Technology in
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The MAD Scientist Network
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2005 NRC Report
Trends & Emerging Challenges
Army Programs Addressing Challenge
Emerging Research Directions
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NRC Report on Network Science
(2005)
“The fundamental components of a network are its structure
(nodes and links) and its dynamics, which together specify
the network’s properties (functions and behaviors). Core
research principles should enable predictions of network
behaviors, given the structure and dynamics of the network
as inputs.”
 Networks have a pervasive influence in all aspects of life
 Fundamental knowledge to predict properties of networks is
primitive
 Research is fragmented with disciplinary stovepipes
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Major Research Challenges
from the 2005 NRC Report on Network Science
Design & Synthesis of Networks to Obtain Desired Properties
Dynamics, Spatial Location & Information Propagation in Networks
Modeling & Analysis of Very Large Networks
Robustness & Security of Networks
6
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Major Research Challenges
from the 2005 NRC Report on Network Science
Abstracting Common Concepts Across Fields
Increased Rigor & Mathematical Structure
Better Experiments & Measurements of Network Science
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2005 NRC Report
Trends & Emerging Challenges
Army Programs Addressing Challenge
Emerging Research Directions
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Trends Impacting Networks
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Network Science Challenges
Commercial vs Military Networks
 Fixed infrastructure
 Mobile ad hoc & hybrid
 Resource-rich
 Resource-constrained
 Limited security constraints &  High levels of security,
Communications
interoperability by standards
 Google search, information
apps rapidly evolving
 Data mining & knowledge
discovery tools
Information
 Pervasive use of social
networking
Social
 Social networking about
connecting with friends/family
coalition interoperability
 Discovery of network
attributes, semantic links,
& structures needed
 Discovery/analytics of more
heterogeneous, noisy,
dynamic, & adversarial nets
 Very limited use of social
networking
 Adversarial social
networking discovery
Increased complexity of design, discovery, prediction, & control
Increased interactions between comms, information, & social networks 10
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Understanding the Interplay Between
Network Genres
 Evolving communication capabilities influence
information collecting, processing, & distributing
 Changes in social network of
sources & analysts
Social
Networks
Information
Networks
 Evolving formal & informal
command structures
 Changes in available information
 Mobility & complex terrain affect connectivity
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Communications
Networks
 Evolving social structures,
influence, & attitudes
 Changing communications structure
 Friendly information & insurgents'
propaganda affect contacts,
perceptions & connections 11
11
Understanding the Interplay Between
Network Genres
Interests dictate sources of info sought:
 Information access changes
opinions, social ties, & network
 Social link formation may be
predicted by analysis of comms &
information behaviors
Comms constrained by social requirements (e.g., chain of
approval). Joint design may improve/disrupt:
 Add new strategic followers to a Twitter network so that
the total time to reach everyone reduces dramatically
 Find best set of social links to thwart so as to fragment
the network  causing long delays
Social
Networks
 Propaganda effects
Information
Networks
Reliability & availability of
communication links affect:
 Social tie strengths
 Trust in information & trust
of decisions
 Team effectiveness
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Smart caching techniques exploit
knowledge of info sources & social
relationships
Communications
Networks
 Caching affects availability/latency
 Which impacts spread of
ideas/information
 And possible fragmentation of
communities
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The Goal:
Exploit Full Potential of Composite Networks
Complex behaviors & interactions of communication, information, and
social-cognitive networks must be understood, anticipated, and leveraged
Assess
How to monitor, measure, & assess the performance
& behavior of composite social/cognitive, information,
& communication networks?
Model
How to understand & quantify the dependencies
& causalities in the complex networks?
Predict
How to predict the networks’ evolution & upcoming
risks (e.g., loss of information quality or trust)?
Influence
How to influence, adapt & optimize the behaviors
of composite networks to support mission success?
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PublicDistribution
Release; Distribution
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For PublicFor
Release;
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2005 NRC Report
Trends & Emerging Challenges
Army Programs Addressing Challenge
Emerging Research Directions
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Micro autonomous
Systems CTA
Cyber Security
CRA - PSU
Control of Complex Networks
Information Engines
UC Davis
15
PublicDistribution
Release; Distribution
ApprovedApproved
For PublicFor
Release;
Unlimited Unlimited
The Network Science
Collaborative Technology Alliance
CERDEC
Low
Understanding
 BBN
(LEAD)
 ArtisTech
 University of California
Riverside
 University of Delaware
Social/Cognitive Networks ARC




Synchronization
Shared Understanding
Rensselaer Polytechnic Institute (LEAD)
Northeastern University
City University of New York
IBM
Distributed Planning
Knowledge Management
Information Networks ARC
Situational Awareness
Availability




U. of Illinois at Urbana-Champaign (LEAD)
University of California, Santa Barbara
City University of New York
IBM
Dynamic
Interoperability
Self-healing
Networks
Communication Networks ARC
www.ns-cta.org





Penn State University (LEAD)
University of Southern California
University of California, Davis
University of California, Santa Cruz
City University of New York
16
ARCUnlimited
= Academic Research Center
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Radios
Spectrum Agility
Sensors
High
Understanding
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Network & Information Science
International Technology Alliance
Collaborative Venture between US ARL, UK Dstl, Academia,
& Industry to enhance our abilities to conduct coalition operations
www.uisukita.org
 Coalition Interoperable Secure
& Hybrid Networks

Hybrid Networks

Security/Network Management & Control

Security for Distributed Services
 Distributed Coalition Information
Processing for Decision Making

Shared Understanding & Info Exploitation

Service Management in Distributed Networks

Exploitation of Distributed & Uncertain Info
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Mobile Network Modeling Institute:
An ARL-led OSD HPC Modernization Program
 Exploit high performance computing to:

Enable DoD to design & test networks at sufficient
levels of fidelity & with sufficient speed

Understand behaviors of network enabled capabilities
 Develop scalable computational modeling tools
 Develop software that transforms the way DoD
mobile networks are designed / used
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ARL Cyber Research & Operations
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Cyber Security (CSEC)
Collaborative Research Alliance
CERDEC
A Collaborative Alliance between ARL, CERDEC, Academia, &
Industry to advance the foundation of cyber science in the context
of Army networks
 Develop a fundamental understanding of
cyber phenomena (incl human aspects)
 Fundamental laws, theories, & theoretically
grounded & empirically validated models
 Applicable to a broad array of Army
domains, applications, & environments
http://www.cra.psu.edu/
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2005 NRC Report
Trends & Emerging Challenges
Army Programs Addressing Challenge
Emerging Research Directions
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Emerging Directions in Network Science
Understand how multi-genre networks behave
over time  dynamics, interactions, &
co-evolution
Understand how to control network behaviors
so that the capacity of the network to deliver
relevant information can be maximized
Understand how information representation,
discovery, & analytics contribute to distributed
understanding & social influence
Understand trust & its impact on distributed
decision-making in the presence of conflicting,
incomplete or malicious information
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Co-Evolution & Dynamics in
Multi-Genre Networks
Optimize for
Mission Success
Co-evolving
Social Network
• Structural
• Interaction
• Flow Properties
Couplings
between complex
networks
Co-evolving
Network
Time-evolving
Properties
Models
Quantify
Dependencies
Deterministic
& Stochastic
Approaches
Co-Evolution & Dynamics of
Multi-Genre Networks
Goal: Understand how multi-genre
networks behave over time and space 
dynamics, interactions, & co-evolution
Two networks embedded
in the same “node space”
S
social
Facebook
A
LinkedIn
Mathematical theory for
“co-evolving” communicationsinformation-social/cognitive networks
B
D
R
Modeling approaches for capturing
structural properties (graph theoretic &
beyond)
Mathematical representations for
analysis & prediction of properties of
networks under various types of dynamics
C
c
a
S
social or
organizational
i3
A i2 B i4 D
CoEv
information
cascade or task
dependency
i3
i1
i2
r
a
b
Networks loosely
embedded
in different “node
spaces”
C
i1
comms
d
i4
Two co-evolving
networks embedded
in different “node
spaces”
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Data-Driven Analysis of Collaboration
Structure and Evolution
State-of-the-Art
 Collaboration networks are captured by graphs
 Groups (teams/cells) are inadequately modeled
by a set of edges
Simplicial complexes are well suited
for capturing group structure in collaboration
networks; simplicial metrics offer new insights.
Technical Contributions
Technical Approach
 Application of simplicial metrics to social networks
in addition to traditional graph theoretic ones, e.g.,
facet degree (# of groups a node is in), holes,
minimal non-faces
 Study higher dimensional structures such as
simplicial complexes and hypergraphs
 New relationships in large-scale collaboration
network (e.g. facet degree vs. size) used as the
basis of generative models
 Analyze real-world data sets using simplicial
metrics, derive insights and algorithms, apply to
military collaboration (e.g. call groups, insurgents)
 Introduction of simplicial collapsing in networks to
characterize fundamental connections that define
the network
Army Need/Benefits
Collaborative teams, both friendly and adversarial, are
ubiquitous in the Army’s operating environment
 Networked teams, insurgent cells, coalitions, adversarial
groups, scientific collaborations, etc.
 Groups of information-social nodes => cross-genre

 Benefits: New insights into cross-genre group phenomena
not discernible by graphs (e.g. missed collaborations)
Moore, Swami, Drost, (ARL) , Ramanathan, Basu
(BBN), Hoang (UCSB); Wilkerson, Krim (NCSU).
BASNA 2013, ICASSP 2013, GlobalSip 2013,
NetSciCom 2012
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Data-Driven Analysis of Collaboration
Structure and Evolution
 Relationships, properties and metrics:
= c x (# vertices)b

# facets

Evidence of power-law with cut-off in facet degree

Relationships between topological “holes” and
minimal non-faces and graph centrality metrics

Metrics for assessing collaboration, and application
to NS-CTA and CN-CTA complexes
 Strong collapsing

Our theory shows: homology and hole location are
preserved; computational complexity reduction
(typically order of magnitude in size); distributable
process

Relevant to existing sensor network coverage
algorithms as well as social network simplification
#facets vs #vertices for
DBLP (~1.2M authors, ~2.2 M papers)
b ~= 0.94
c ~= 1.09
Computational Gains
Conjugate Complex
Original Complex
Discovered relationships within large
collaboration networks that led to new generative
models; strong collapsing discovers core network
–defining connections & eases complexity
#facets vs #vertices for
IMDB (~0.8M persons, ~0.12 M movies)
b ~= 1.03
c ~= 0.10
DBLP 2-holes vs 2-MNFs
Complex after one
iteration
Iterate until the complex stabilizes
Strong Collapsing Process
The use of simplicial metrics and techniques for efficiently analyzing real-world collaboration
networks can lead to new insights and help in devising random generative models.
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Finding “Most Private” Paths in Networks
State-of-the-art
 Shortest path problems on static and dynamic
graphs (pre-CTA) and hypergraphs (in CTA) wellunderstood for additive path metrics
 Thinnest Path Problem: Given a source and a
destination, find a path with minimum number of
nodes overhearing the message: metric is nonadditive, and graph model insufficient
Our directed crosses, exposed disk HG,
and bounding techniques may provide tools for
complexity studies in geometrical HG and graphs.
Pushes state-of-the-art in minimum LPx routing
- Nest backward induction algorithm is optimal
with O(n) time complexity in 1-D / 1.5D
- Two polynomial-time approximation
algorithms for 2-D networks with ratios
Technical Approach
 Use directed hypergraph model to cope with
variable transmit powers (range)
 Establish realizability of MDS reduction
 Develop approximation algorithms
Army Need/Benefits
 Quantify physical layer security: LPI/LPD concerns
 Energy-efficient communications
Gao, Zhao (UCD) , Swami (ARL)
Thinnest path problem is polynomial time
in 1-D and 1.5-D networks; NP-complete in 2-D
networks even under unit disk model.
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(Allerton 2012, IEEE/ACM ToN, 2014)
Trust in Networks
Understand trust & its impact on
distributed decision-making in the
presence of conflicting, incomplete
or malicious information
Modeling and measuring trust from the
perspective of the trustor, taking into
account the interactions among network
layers
Predictors of trust & distrust and
techniques to enhance or exploit them
Mathematical theory for impact
of trust on network evolution such as
in an information-sharing network
31
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Trust in Distributed Decision-Making
State-of-the-Art
 No models and experimental paradigms for
studying impact of organizational structure & rules
& trust in epistemological decision-making
 Trust models tend to concentrate on trust as a
single construct, but information based decisionmaking involves information credibility, as well as
reliability and competence of team members
Technical Approach
Create a unified operational scenario to study trust in
distributed networked decision-making by taking into
account:
 Communication networks: bandwidth &
connectivity
Decision-making in networks involve people as
routers and filters: trust for receiving both timely
and correct information must be balanced
Technical Contribution
 Development of parallel agent models &
experimental paradigm based on a joint operational
scenario enables analysis of performance in multigenre networks
 Model considers individual behaviors of network
nodes: competence, willingness, confidence and
reliance on network nodes through trust or
organizational role.
 Information networks: information processing
 Cognitive networks: limited attention & ability
 Social & organizational networks: dependence on
team members to solve problems
Army Need/Benefits
Develop effective multi-genre methods to enhance
trust and improve decision-making in networks
K. Chan (ARL), J.H. Cho (ARL), S. Adali (RPI)
ARL, RPI, CUNY, USMA
32
Trust in Distributed Decision-Making
What do I know?
What do others know?
Information
SA
Information
Flow
Evaluate
Evaluate
Information Information
behavior
credibility
Decision=
making
Team SA
Trust
Who is cooperating
with me and how?
Trust-based
Decisions:
Use of trust
to dictate
information
sharing
decisions
offers greater
resilience to
misbehaving
nodes
situation awareness
1
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
Network Structure:
Increased
willingness and higher
connectivity
organizational
connectivity improves
edge SA and fault tolerance
collaborative but incurs high
coordinated
communication costs.
willingness
[Chan, Cho, Adali, BRiMS 2013]
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.7
0.6
0.5
0.4
without trust
with trust
0.3
0.2
0.1
0
1
2
3
4
5
6
7
8
9
10
selfishn0 nodes
[Chan, Cho, Adali, ATC 2012]
Create new
information flow
0.9
0.8
SA
awareness
situation final
Information Flow & Decision-making
Credibility:
Experimental
approach to
understand
information
processing and
source
evaluation
Trust captured along reliability under time pressure & competence of processing information
allows us to study epistemological decision-making in many different networked scenarios.
33
Network Tomography
Infer internal network state from external aggregate measurements.
Network
state:
w
R
Measurement:
E
w
=
c
F
probe
G
probe
w1
H
w2
c
Q(w)=c
 Examples:
 Estimate link metrics from path metrics
• ICMP-based tools such as ping, traceroute require cooperation,
may be inaccurate (route asymmetry, control/data pkt priorities)
 Localize faulty links/nodes from e2e connectivity
 Infer statistical link characteristics (e.g., loss rate)
Under what topological conditions does invertible R exist ?
If it exists: How to efficiently find and invert such an R ?
Identifiability Results
Type of
path
#monit
ors
Condition for identifiability [1]
Cycle
free
paths
2
Entire G: impossible
≥3
Gex is 3-vertex-connected
1. Spanning-Tree based path construction:
constructing matrix R
2. Efficient inversion of |L| x |L| matrix R
...
3-vertexconnected:
delete 2
nodes -> still
connected
• Complexity: O(|V|·|L|)
• Complexity: O(|V|+|L|)
- Yields a minimum monitor placement
- Yields a minimal set of paths (R is square)
[1] Ma, He, Leung, Towsley, Swami, ACM/USENIX IMC, 2013.
[2] Ma, He, Leung, Towsley, Swami, ACMICDCS 2013, Philadelphia (BPA)
35
Partial Network Identifiability
Network is usually NOT fully identifiable
Monitor locations do not satisfy
“Gex is 3-vertex-connected”
Limited budget (k monitors) for
deploying monitors k<kMMP
Which links are identifiable under
given monitor placement?
Algorithm w complexity (|V|+ |L|)
|L|
kMMP
How to place k monitors optimally
when k is not sufficient?
Greedy algorithm
Related Work:
Fault localization: w La Porta, Tati, Silvestre
Measurement Design w Towsley, Chang et al.
[3] Ma, He, Leung, Towsley, Swami, INFOCOM 2014
36
http://www.arl.army.mil/www/pages/172/docs/ResearchARL_March2013.pdf
37
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