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Social Cognitive Network
Academic Research Center
Goals:
– Leading research on social and cognitive
Military Needs:
– Military missions bring highly organized
and disciplined soldiers to loosely
organized local societies with different
cultures, beliefs and value systems, with
often hostile attitude to the mission goals.
To achieve long term goals of the
mission, soldiers need to understand
dynamics and structure of social
networks that they face. Our research is
creating such understanding.
aspects of network science
– Contributing social aspect and cognitive
limitations for interactions between humans and all
genres of networks (communication, information
and social)
Center Focus:
– We focus on human network interactions,
–
foaf:knows/foaf:based near social network,
western hemisphere
from the individual cognitive abilities to
the group interactions in social networks
that impact and are impacted by
communication and information flow in
groups and societies
We investigate social networks ranging
from formal (military, workplace), informal
(social, family interactions) to hidden and
adversary networks (insurgents, political
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opponents, secret societies)
Research Expertise
Research Area:
Accolades:
– Albert Laszlo Barabasi (Northeastern U),
– Institution
• Rensselaer Polytechnic Institute, lead member, APS Fellow, foreign member of Hungarian
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focuses on network discovery and evolution,
cognitive aspects of networking, valuation of
–
network interactions, and trust
Northeastern University, member, focuses on
network science and human mobility
IBM Research Laboratory, member, focuses on –
valuation of network interactions
CUNY, member, focuses on trust and cognitive
aspects of networking
–
MIT, focuses on social incentive in networks
University of Notre Dame, focuses on trust and
network dynamics and evolution
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Indiana University focuses on spread
processes in networks
Northwestern University focuses on trust in
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network interactions
University of Maryland focuses on trust in
–
decision making
Academy of Sciences and Academia
Europaea, Distinguished University Professor
Wayne Gray (RPI), Cognitive Science Society
Fellow, Humboldt Research Award from the
Alexander von Humboldt Foundation.
James Hendler (RPI), IEEE Fellow, Web
Expert for US Data.gov White House project,
Senior Constellation Professor
Alex Pentland (MIT), Design Futures Council
Senior Fellow, Director of the Human
Dynamics Lab
Bolek Szymanski (RPI), IEEE Fellow, foreign
member of Polish Academy of Sciences,
Distinguished Professor, RPI
Alex Vespignani (Indiana U), APS Fellow,
Maria Curie Fellow, James H. Rudy Professor
Al Wallace (RPI), IEEE Fellow, IEEE Third
Millennium Medal, Yamada Corporation
Professor
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Research Thrusts
Network Discovery (with IRC, INARC, and EDIN):
– we focus on hidden networks as well as networks actively seeking camouflage, on discovery of
such networks based on partial clues and on correlation between social, information and
communication links in complex hidden networks
Networks Robustness (with CNARC and INARC):
‒ our focus here is on controllability of networks and the relation between the structure of the
network and its vulnerability to cascading failures as well as on mobility impact on caching and
routing in communication networks
Market-based Algorithms (with IRC and CNARC):
‒ we focus on social incentives and impact of social community on valuation of the awards, we
also investigate incentive of participatory sensing, fact-discovery and reporting of findings
Engineering Network Evolution (with EDIN):
‒ modeling the evolution and growth of dynamic composite networks --- from link prediction to
discovery of emergent groups
Cognitive Aspects of Networking (with Trust, ARL):
‒
we investigate short term (0.3-3sec) cognitive processes in humans interacting with other
humans via networks and on network technology to improve human performance in network
environments
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Our research in the context
of the NS CTA Roadmap
Detect hidden
and overlapping
social networks
using statistics
(to ensure
scalability) of
observable
communications
and externals
events
Provide socially
based incentives
for participatory
measurements
and factreporting with
overprovision of
participants
Combine organizational
data, communication and
information network use
with inconsistent selfreported social
interaction data to
discover and measure
workspace social
networks
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Our research in the context
of the NS CTA Roadmap
Use different models
of opinion spreading
fitted to real data to
create and test
predictive models of
evolution of opinions
in a diverse society
under influence of
social groups and
media
Using weighted
network models,
characterize
structural
properties of
cascades of
failures in
networks of all
genre under stress
Classifiers/models to
predict link formation and
persistence, which may
indicate (node) volatility
and changes in network
structure
Develop models of
human mobility based
on social ties and
obligations and use
sophisticated
techniques (entropy
based, probabilistic
grammars) to fit such
models to real data
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Our research in the context
of the NS CTA Roadmap
Use social pressure
index and mobility
models to optimize adhoc, social and military
information gathering
and routing networks
to reduce bandwidth
demands and enhance
their performance
Expand and
enhance current
understanding of
network
controllability in
all genres of
networks in
diverse
scenarios
Adapt the placement
and ranks of
committed members
of the society to
enhance impact of
their opinions
Engineer network
interfaces to match the
volume and display of
network information to
the cognitive
capabilities of humans
to optimize situation
awareness in combat
and high stress
scenarios
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S2.1. Adversary Social Networks:
Detection, Evolution, Stability and
Hierarchy
Key Objectives:
Different colors are different
types of nodes, eg.people,
events, locations.
Partial Cue
Parent Network
Identify hidden adversary networks from analyst partial cues.
Grow partial cues to full communities with internal community hierarchy
and information pathways.
Identify influential members and stable cores.
Identify community evolution and relationships between communities
(e.g. opposition versus trust).
Deliverables:
Y2: Developed preliminary indexing schemes for social networks to find
fragment queries. Developed preliminary framework for studying
evolution. Developed new methods for community detection using
Overlapping Communities
graph embedding.
Y3-Q2: Report on testing of indexing methods and evolution detection
methods. Comprehensive study of community detection.
Y3-Q4: Preliminary methods to handle missing links due to inability to
detect full adversary networks.
Key Technical Innovations
Impact:
• Efficient social indexing for partial adversary network Discovery of hidden adversary networks from analyst partial cues from
vast noisy communications; their evolution, their interquery in social semantic databases.
relationships, and stable cores.
• Use statistical interaction analysis to identify hidden
networks.
Role
Researchers
• Allow networks to overlap, defining relationships
Lead
M. Magdon-Ismail, RPI, SCNARC
between communities. Identify the hierarchies.
Primary
M. Goldberg, RPI, W. Wallace, RPI, N. Chawla, ND, SCNARC
• Define a community as a locally optimal set.
Collab.
B. Uzzi, NWE (SCNARC), S. Adali, RPI (SCNARC), D. Roth, UIUC
• Use streaming interactions, not network snapshots.
(INARC), A. Kementsietsidis, IBM (INARC), J. Han, UIUC (INARC),
X. Yan, UCSB (INARC), G. Korniss, RPI (SCNARC), B. Szymanski,
• Validation on multiple scale electronic networks.
RPI (SCNARC), Z. Wen, IBM (SCNARC)
S2.2: Forming, Dissolving, and Influencing
Communities in Social Networks
Collaborations:
Key Objectives:
• To help counterinsurgency operations in IW, we will develop
and employ individual-based models to investigate social
influencing and associated strategies in social networks.
Military Relevance:
• “insurgency and counterinsurgency operations focusing on the
control or influence of populations, not on the control of an
adversary’s forces or territory” (IW, JOC, DoD, v.1.0, 09.11.07)
• “Arguably, the decisive battle is for the people’s minds”
(COIN FM 3-24)
Impact:
• Our methods and models for community detection, community
stability, and social influencing will be applicable to data sets of
diverse types, spanning all scales (ranging from a few hundred
to millions of nodes/links), including those collected by the
military.
Key Technical Innovations
• Develop individual-based models and methods to identify
and influence communities in social networks, which
manifestly emerge as the result communication and
information flow across the links
• Formulate mathematical foundation of connections between
meta-stable configurations in social dynamics and
underlying network communities
• Design efficient methods and algorithms for social
influencing in social networks with community structure
(max-k domination, k-shell decomposition)
• Dr. S. Sreenivasan (RPI) collaborates with V. Kawadia (BBN)
and S. Pandit (ND) at the NS-CTA facility at BBN on LPA-based
community detection in dynamic time-evolving networks
• Data sharing: RPI (AddHealth high-school friendship networks
~O(100-1000) nodes); CUNY (LastFM.com online friendship
network, ~2mill nodes); NEU/ND (mobile-communication graph,
~4mill nodes)
• Organized Workshop at NetSci (June 7, 2011)
Accomplishments:
• Discovered the emergence of tipping points in social influencing
• Analyzed the impact of time delays in info-social networks
Road Ahead:
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Incentive-based influencing and spreading
Competing and conflicting committed sets in social networks
Efficiency of influencing with committed agents vs. mass media
Combination of geographical and network effects
Role
Researchers
Lead
G. Korniss,(RPI )
Primary
B. Szymanski (RPI), C. Lim (RPI),
H. Makse (CUNY), Z. Toroczkai (ND), A.-L. Barabasi (NEU)
Postdocs
Sreenivasan, Asztalos (RPI);
Gallos (CUNY); Pandit (ND); Song (NEU)
Grads
P. Singh, D. Hunt (RPI); W. Zhang (RPI)
Collaborators
V. Kawadia (BBN), B. Uzzi (NW), N. Chawla (ND),
A. Pentland (MIT), M. Magdon-Ismail (RPI)
Formation of Hidden Groups around Events
State-of-the-Art
 We are only beginning to collect data on
social networks that form in response to high
impact events

Katrina, SD 2007 Fires, Japan Tsunami
 Little understood about the dynamics of
formation and final structure of such
networks.
S
By tracking streaming interaction
(communication) patterns, we can identify the
emergence of such a hidden group. By
identifying the important structural links we may
either encourage or hinder the formation of the
spontaneous network.
Research Problem:
•Understand the creation and the evolution of
communities in social media in order for
adversary networks to be discovered
• Identify the crucial links which should either be
enhanced or hindered.
A spontaneous network forms around event S.
The network could be adversarial.
Army Need/Benefits
•Study naturally occurring networks that
evolve in response to events.
•Facilitate discovery using large scale
participatory sensing (e.g. Twitter)
Such adversary networks may form in response
to military events or rumors (for example a
rumor that some US embassy is housing some
militant). How should one deactivate such a
network. Which are the important links?
Wallace (RPI)
Magdon-Ismail, Goldberg (RPI), ARL
(SWDM 2012 at WWW
2012)
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Formation of Hidden Groups around Events
Network evolving in response to event
tends to have a core plus satellite interactions that
are mostly composed of dyads. The result appears
to hold across different types of networks in
response to different types of events.
Case Studies:
Empirical observations:
•Very similar evolutions to similar final states for
two very different case studies.
•Both networks evolved to a state with a core
and sparse satellite islands of communication
that didn’t take-off
RPI Burglar alert
Courtesy of
imagegossips.com
Japan tsunami, 2011
Twitter re-tweet
graphs
Long-Term Goals
Develop methods to identify the core formation
early, by extending ideas from our research on
identifying hidden groups in streaming data to
real time monitoring settings.
Develop methods to understand the important
links in the formation of such networks which
should be targeted either to destabilize or
enhance the network.
Preliminary results that indicate similarities in how network form in reaction to an event. Early
identification of the core can have implications for being able to control such networks.
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Formation of Hidden Groups around Events
Behavioral Aspects
Event/Warning
– prescribed
behavior
Tweet with
behavioral
intent
Confirmation
of behavior
•Event drives a warning and prescribed action
•Tweets can be found around the event that exhibit the action
•Behavioral onsite data that can generalize and confirm that action has been taken
•Example:
• Event/warning • Japan Earthquake/Tsunami – warning issued in Hawaii and evacuation was
ordered
• Tweet • "RT @xxxxxxxx: Prey for my brother his wife and son as they take shelter in
Hawaii :("
• Confirmation of behavior • Based on the information provided by Hawaii Civil Defense there were multiple
shelters opened in Hawaii and the count of the people at the shelters was
obtained
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Formation of Hidden Groups around Events
 Future Research Direction
 Develop methods to identify the core formation of a hidden
group early in its evolution, by extending ideas from our
research on identifying hidden groups in streaming data to real
time monitoring settings.
 Relevance to Network Science
 Studying the dynamics of coordination of networks during
events gives insight into the formation of ‘goal oriented’
spontaneous communities.
 Link formation is at the core of social network evolution, and link
prediction algorithms give insight into link formation.
 Relevance
 Ability to discover hidden (especially deceptive) groups which
are the most likely to be adversary is useful information to
ARMY analysts.
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Social Cognitive Network
Academic Research Center Overview
Boleslaw Szymanski
[email protected]
11 March 2012
Highlight 1
Institutions
STATUS QUO
Lead PI: Alex Pentland, MIT
–Social mechanisms such as
taxation and subsidies do not
utilize the value in social
relationships
–They are often both costly to
implement and less effective
How it works in two different contexts
Crowd-Sourcing:
Reward is given to
referral chains as well as
targets to maximize
diffusion (DARPA 40th
anniversary of Internet
Grand Challenge)
NEW INSIGHTS
Carefully designed mechanisms
leverage the power of social
network:
Peer-Pressure:
–Subsidies are given to
Individuals based upon
–(Crowd-Sourcing) The small-world
the performance of their
connectivity of social network
Peers.
–(Peer-Pressure) Social capital in
relationships can be used to
significantly amplify the effect of
subsidies
–Thus, individuals have an incentive to exert
pressure on their peers to perform well.
–The peer-pressure has a stronger effect than direct
subsidies to individuals based upon their
performance.
Social Incentives to Shape Social Networks
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LONG TERM GOAL
.
QUANTITATIVE IMPACT
Highlight 1 Results
8:52
Group 1: Reward individual for behavioral
change; Group 2: Our idea of rewarding friends.
Peer-Pressure: 2x improvement in
Red Balloon Experiment: Well sustained
diffusion for MIT team on the Internet, and fitness & activity levels when using
the eventually winning in less than 9 hours. our mechanism as compared to
traditional Pigouvian subsidies

A comprehensive theory of incentive mechanism design on networks
leveraging social ties for greater efficiency with applications to military
teams training/combat and in the Army led projects with civilian participation

Infrastructure for large-scale deployment and testing of the theory
including military camps and theaters of operation
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Highlight 2
Institutions
STATUS QUO
Lead PIs: Gyorgy Korniss, Bolek Szymanski, RPI
Social Influence and opinion dynamics
on networks:
Predominantly in studies so far:
• All nodes equally influencable.
• No discussion of consensus engineering
Main Result:
Committed individuals randomly chosen
from the population can guarantee a rapid
change in the prevailing dominant opinion,
so long as they constitute at least 10% of
the population.
Small committed minority is enough to
make a big change.
Model and Methods:
• Model for social influence:
Binary agreement model
NEW INSIGHTS
Engineering consensus on social networks:
• Two competing opinions
(currently prevailing vs. minority hold)
Q. How do opinions change in the presence
• Some fraction of nodes are committed individuals
of individuals committed to an opinion?
Committed individuals never change their opinion.
Key new insight:
A sharp transition in the time needed by
committed individuals to win over the
population, as their number is varied.
• Simulate the model and use analytical arguments.
Assumptions:
All committed influencers proselytize new opinion
“Never doubt that a small group of thoughtful, committed, citizens can
change the world. Indeed, it is the only thing that ever has.” - Margaret Mead
Highlight 2
A New (minority hold) opinion
Frac. of traditional opinion holders
B Old (currently prevailing) opinion
For two different network types:
After 10000 time steps
Initially only the committed
individuals hold opinion A
Q. How does consensus time
(time to
reach all-A state), depend on
percentage of committed individuals?
Q. Beyond what committed fraction
size
is consensus engineering
scalable? (i.e. time to consensus
grows slowly with network size)
p < pc
p > pc
Fraction of committed agents
Critical fraction
≈ 10%
To engineer consensus in feasible time,
no more than 10% of committed
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individuals are required.
QUANTITATIVE IMPACT
Highlight 2 Results
We study the physics of how rapid consensus to a new opinion can arise
We discovered a concrete lower bound in the number of “committed individuals"
needed to rapidly create consensus:
•Critical fraction of individuals required ≈ 10%
•No knowledge of social network required: committed set can be randomly chosen.
LONG TERM GOAL
The path forward
We aim to devise algorithms to speed up adoption of progressive and/or antiinsurgent influence through social networks given:
 Full knowledge of the social network
 Partial knowledge of the social network
 Constraints on number of "committed individuals“, e.g. due to a limited budget.
Quantitative science of social engineering
Arguably, the decisive battle is for the people’s minds” -COIN FM 3-24
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Highlight 3
Institutions
NEW INSIGHTS
STATUS QUO
Lead PI: Laszlo Barabasi, Northeastern U.
A system is controllable if it can
be driven from any initial state to
any final state in finite time.
A framework to study the
controllability of complex selforganized systems was lacking.
Combining the tools of control
theory and network science opens
new avenues to deepening our
understanding of complex systems.
Main Result:
We can indentify the minimum set of driver nodes
to fully control the system’s entire dynamics.
How It Works:
We rigorously proved the minimum inputs theorem
based on an elegant mapping between structural
controllability and maximum matching.
Assumptions and Limitations:
We assume that link weights are either fixed zero
or independent free parameters.
We discovered a novel theorem that enables identifying the minimum set of nodes
through which the control input can steer the network to the desired future state.
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Highlight 3
Application: How to find the minimum set of nodes to control the whole system?
Complement of a maximum matching (a set of edges without common heads or
tails) is the minimum set of nodes through which network can be controlled.
LONG TERM GOAL
QUANTITATIVE IMPACT
Highlight 3 Results
We address controllability for arbitrary
network topologies and sizes and find
that:
1. The minimum set of driver
nodes can be exactly identified,
with ND = max{1, Nunmatched}.
2. ND is mainly determined by the
network degree distribution.
3. Driver nodes tend to avoid hubs.
 Effects of higher order correlations (degree
assortativity, clustering coefficients, modularity,
hierarchy structure) on network controllability.
 Robustness of network controllability against both
random failure and intended attack.
 Optimal design of network structure considering
both controllability and robustness.
To appear on May 12, 2011
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Closing Remarks
Social and cognitive aspect of networking are an important component in
understanding all genres of networks and discovery of laws of network science
Key Publications: ( all inter-center)
Le, H., Pasternack, J., Ahmadi, H., Gupta, M., Abdelzaher,
T., Han, J., Roth, D., Szymanski, B., Adali, S. 2011. Apollo:
– Market-like Processes in Networks: SCNARC(MIT, Towards Fact-finding in Participatory Sensing. ACM/IEEE
RPI), IRC(Harvard, BBN, U Michigan), CNARC(PSU) IPSN (Demo Paper)Reference (INARC, SCNARC)
–
Srivastava, M., Abdelzaher, T., Szymanski, B. 2011. Human
– Network Discovery: SCNARC(RPI, ND), IRC(BBN), Centric Sensing. Philosophical Transactions of the Royal
INARC(UIUC, CMU)
Society (INARC, SCNARC)
–
Tong, H., Prakash, B., Tsourakakis, C., Eliassi-Rad, T.,
– Cognitive Aspects of Networking: SCNARC(RPI,
Faloutsos, C., Chau, D. 2010. On the Vulnerability of Large
CUNY), INARC(PARC), ARL
Graphs. ICDM 2010 (IRC, SCNARC)
– Valuation of Network Interactions: SCNARC(IBM, – Valler, N., Prakash, B., Tong, H., Faloutsos, M., Faloutsos,
C. 2011. Epidemic Spread in Mobile Ad Hoc Networks
RPI, ND, NEU), INARC(UIUC), CNARC(PSU)
Determining the Tipping Point. IFIP Networking 2011 (IRC,
SCNARC)
Metrics (for FY11 only):
–
Zhuo, X., Cao, G., Szymanski, B., La Porta, T. 2011. SocialBased Cooperative Caching in DTNs: A Contact Duration
– Papers: Journal/Chapter (17), Conference (22)
Aware Approach. SECON 2011 (CNARC, SCNARC)
– Ph.D. Students On Board (21) Postdocs (7)
–
Pandit, S., Yang, Y., Kawadia, V., Chawla, N., Sreenivasan,
– Ph.D. Students Graduated (2)
S. 2011. Detecting Communities in Time-evolving Proximity
Networks. IEEE Network Science Workshop (IRC, SCNARC)
–
Yang, Y., Sun, Y., Pandit, S., Chawla, N.V, Han, J. 2011. Is
Objective Function the Silver Bullet? A Case Study of
Transitions:
Community Detection Algorithms on Social Networks,
– In planning
ASONAM, 2011. (INARC, SCNARC)
Key Collaborations Enabled:
–
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S1.2: Social Incentives to Shape
Network Actions and Responses
Efficient Incentives for Network with “Selfish” Agents
Y2 Accomplishments:
An optimal recruitment mechanism for DARPA challenge
campaign
A new, efficient incentive mechanisms for network exchanges
Congestion control auction for bursty network traffic from
tracking mobile targets
Deliverables:
Q2. Formal analysis and experiments on role of peer-pressure
on network performance; auction parameter sensitivity for
participant selection
Q4. Impact of balanced referral chain and provider awards on
campaign performance; benefits of balanced peer-pressure
and individual awards for network performance
Key Technical Innovations
- Design of participation preserving incentive compatible auction
mechanisms for dynamic and opportunistic selection of the
agents and/or sensors for data collection
- Study of incentive systems for recruitment and sustainable
participation in crowd-sourcing campaigns
- Research on reward systems for peers in behavior modification in
social networks
Key Objectives:
- Efficient and sustainable incentives for recruitment and sustainable
participation of network members in crowd-sourcing campaigns
- An auction mechanism for pricing participatory sensing by devices
embedded in the cell phones of the civilian population
- Peer-based reward system for behavior modification in social
networks
Impact:
An integration of participatory sensing/monitoring and routing to
enable better use of resources and unobtrusive and difficult
to detect civilian participation in the anti-insurgent
campaigns.
Novel incentive and reward mechanisms for participation and
network exchanges with “selfish” (non-collaborative) nodes
.
Role
Researchers
Lead
B.K. Szymanski, RPI, SCNARC
Primary
A. Pentland, MIT, SCNARC
Collab.
M. Wellman, U. Michigan. IRC, V. Kawadia, BBN, IRC,
A. Goel, Stanford. CNARC. T. Abdelzaher, UIUC,
INARC.
S1.3: Socially-Driven Models of Human
Mobility and Its Use in Delay Tolerant
Routing
Less noise but
fewer key fact
More noise
and key
facts
Less noise but
fewer key fact
Key Objectives:
Methods to capture the sophisticated relations between
nodes that define human mobility patterns.
Understanding the interplay between physical space and
temporal interactions on social networks
Deliverables:
*Significant difference
throughout
Less noise but
fewer key fact
Exposure to key facts and “noise” in hierarchy
Last Year Accomplishments
- Analysis of network dynamics and spreading
processes and understanding how does the timing of
relationships affect spreading
Q1: Visualization for small scale episodic network data
Q2: Usage of PCFGs for human mobility modeling in
different scenarios
Q3: Measurement of impact of structural features of
temporal proximity on spreading/routing
Q4: Evaluation of efficiency improvement from human
mobility modeling in participatory data collection,
sensing and routing
- Defined Social Pressure Metrics (SPM) for Routing and Impact:
Caching to represents human mobility
- Ability to analyze the impact of mobility on network
structure
- Investigated participatory sensing and fact-finding
Key Technical Innovations
- Methodology to use Probabilistic Context Free Grammars
for strong predictive models for capturing human mobility.
Analyzing finer granularity of information on physical
proximity to understand spreading processes where
dyadic probability of diffusion is very high.
Investigating methods for identifying the distinct
archetypes that characterize communication patterns
Role
Researchers
Lead
D. Lazer, NEU & Harvard, SCNARC
Primary
B. Szymanski, RPI, SCNARC
Collab.
T. La Porta, PSU, CNARC, G. Cao, PSU, CNARC,
T. F. Abdelzaher, UIUC, INARC, M. Srivatsa, IBM,
INARC, A. Pentland, MIT, SCNARC
S1.4: Composite Networks in
Organization and Team Performance
Key Objectives:
- (T1) Characterization. Understand how the structure of
composite networks in organization affects the team
performance.
- (T2) Prediction. Investigate what kinds of network metrics
are crucial to team performance and how to predict the
performance of a given team before they actually work on
the assigned task.
- (T3) Formation. Automatic team forming recommendations
to optimize the team performance based on network
metrics
Individual Performance (measured on the individual
revenue) after using organizational social network
analysis tool (SmallBlue). We observed an 11% increase
of performance enhancement. We shall further discover
network factors for team performance..
Key Technical Innovations
- Novel causality understanding on how networks
impact team performance, through objective
observation and measurements.
- Novel Predictive models to forecast the
performance of a given team based on its
collective network structure..
- Novel algorithms to automatically find a good
team of experts to perform a particular task, by
optimizing the expertise composition, combined
social networks, and historical and predictive
network evolution. \
Deliverables:
Q1: Investigating team performance characterization.
Q2: Design algorithms for team formation.
Q3: Investigating dynamic aspect of performance.
Q4: Implement and test the algorithm for team formation.
Impact:
- Ability to utilize networks to improve team performance and
team formation.
Role
Researchers
Lead
H. Tong, IBM, SCNARC
Primary
K. Ehrlich, W. Zhen, C.-Y. Lin, IBM, S. Wasserman,
Indiana U, R. Cross, UVA, SCNARC
Collab.
L. Wu, U Penn, SCNARC, X. Fan, UCSB, INARC, D.
Wang, NEU, SCNARC
S2.3: Controllability of Complex Networks
Finding Control
Nodes in the Network
Deliverables:
Q1. Analytical results on calculating single node controllability
and the role of controllability in information propagation
Q2. The role of network evolution on single node controllability
and spreading and control in modular networks
Q3. Validation of the developed approach on real network data
Q4. Validating single node controllability, summary of the impact
of network evolution on controllability and the relation
between nodes controllability and nodes relevance in
information diffusion processes
Impact:
Single node controllability will offer tools to establish the
influence of individual nodes
Novel algorithms will enable better control of the spread of ideas
and pathogens
Key Technical Innovations
- Combine classical engineering control theory with network science Understanding controllability of self-organizing networks and
system will improve our ability to “influence, adapt, and
approaches to dynamic processes on networks to develop
optimize the behaviors of composite networks to support a
fundamental understanding of network controllability
mission's success”
- Study the impact of the network structure in which a node is
Role
Researchers
embedded on this node ability to control the network dynamics
- Combine formal analysis, predictive numerical and computational
Lead
A.-L. Barabasi, NEU, SCNARC
model and empirical data for all genres of networks from
J. George, Civ, ARL/SEDD, J.-J. Slotine, MIT,
communication to information to biological and to social systems in Collab.
L. Adamic, U Michigan, INARC
analyzing network controllability.
Key Objectives:
- Mathematical underpinnings of single-node controllability
- Controllability and communication/information spreading processes
- Evolution of controllability in self-organizing networks