Scalable Methods for the Analysis of
Network-Based Data
MURI Project: University of California, Irvine
Principal Investigator: Padhraic Smyth
Kick-off Meeting
November 18th 2008
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 1
Goals for Today’s Meeting
• Review overall goals and research of MURI project
• University research groups
– learn about each other’s research
– See opportunities for collaboration
Butts
• MURI team and ONR/Navy
– MURI team: learn about ONR interests
– ONR: learn about expertise and plans of MURI team
• Action items
– Future meetings and collaborative activities
– Review Year 1 research goals
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 2
Outline
• Introductions
• Review today’s agenda
– Schedule of talks
– Logistics
• Overview of our MURI research project
– Themes and goals
– Tasks
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 3
MURI Investigators
Padhraic Smyth
UCI
David Eppstein
UCI
Mark Handcock
U Washington
Carter Butts
UCI
Dave Mount
U Maryland
Michael Goodrich
UCI
Dave Hunter
Penn State
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 4
MURI Project Participants
• Postdocs
– UC Irvine
• Romain Thibaux (Computer Science)
• Graduate Students (all UC Irvine)
– Computer Science
• Darren Strash
• Lowell Trott
– Statistics
• Chris DuBois
– Social Science
•
•
•
•
Chris Marcum
Lorien Jasny
Emma Spiro
Zack Almquist
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 5
Outline
• Introductions
• Review today’s agenda
– Schedule of talks
– Logistics
• Overview of our MURI research project
– Themes and goals
– Tasks
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 6
Agenda for MURI Kickoff Meeting at UC Irvine
November 18th 2008
Location: UC Irvine, Bren Hall, Room 4011
MORNING SESSION
8:30 Arrive, continental breakfast available
9:00 Introductions and overview of MURI proposal
Padhraic Smyth (UCI)
9:30 Research and application challenges from ONR's perspective
Martin Kruger (ONR, MURI Program Manager)
10:00 Brief discussion/Q&A session between ONR representatives and PIs
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 7
Agenda for MURI Kickoff Meeting at UC Irvine
10:15 Break
10:30 Tutorial Session: Statistical models for network data:
Mark Handcock (U Washington), Carter Butts (UCI),
Dave Hunter (Penn State)
- Fundamentals of exponential family random graph models (ERGMs)
- Parameter estimation in ERGMs: principles and computational challenges
- Alternative statistical frameworks such as latent space models
LUNCH
12:00 Lunch for PIs and UCI visitors at the University Club
(next door to Bren Hall)
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 8
Agenda for MURI Kickoff Meeting at UC Irvine
AFTERNOON SESSION: 15-minute brief Research Presentations:
1:30 - Studying networks through an algorithmic lens
Michael Goodrich (UCI)
1:45 - Fast algorithms for computing network statistics:
David Eppstein (UCI)
2:00 - Data structures for dynamic and kinetic multidimensional point sets:
Dave Mount (U Maryland)
2:15 - Modeling dynamic and relational event data:
Carter Butts
2:30 - Statistical modeling of large text collectons:
Padhraic Smyth (UCI)
2:45 - Modeling partially observed network data:
Mark Handcock (U Washington)
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 9
Agenda for MURI Kickoff Meeting at UC Irvine
3:00 Break and Informal Discussion
3:30 Brief talks on Software and Data Sets:
- R software for network analysis:
Dave Hunter (Penn State)
- Experimental results on real-world networks
PhD students from Carter Butt's group (Sociology Dept, UCI)
- Large network data sets for experimentation:
Chris DuBois (PhD student, Statistics Department, UCI)
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 10
Agenda for MURI Kickoff Meeting at UC Irvine
4:15 Open Discussion on Research Plans
- relation of MURI research topics to military applications
- further opportunities for collaboration within the team
- year 1 research goals
5:00 Organizational Issues and Wrap-up
- future meetings (frequency, location)
- encouraging interaction between team members
(conference calls, weekly research meetings, etc)
- use of collaborative Web pages
- action items
5:30 Adjourn
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 11
Logistics
• Meals
– Lunch at University Club - for PIs and non-UCI folks
– Coffee breaks
• Wireless
– Should be able to get 24-hour guest access
• Slides
– Will be available online by the end of today
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 12
Outline
• Introductions
• Review today’s agenda
– Schedule of talks
– Logistics
• Overview of our MURI research project
– Themes and goals
– Tasks
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 13
MURI Project: Scalable Methods for
Analysis of Network-Based Data
• 4 universities collaborating, 7 PIs
– Support for (approx) 8 graduate students and 3 postdocs
or research associates
• 3-year project with possible extension to 5 years
• Time Period
– Funding arrived at UCI in September 2008
– At other universities in Sept/Oct 2008
– Official project start/end
• June 1 2008 to May 30 2011/2013
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 14
A Small Social Network
Eppstein
Butts Butts
Mount
Hunter
Smyth
Handcock
Goodrich
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 15
A Small Social Network
Eppstein
Butts
Hunter
Mount
Smyth
Handcock
Goodrich
Statistics
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 16
A Small Social Network
Eppstein
Butts
Hunter
Mount
Smyth
Handcock
Goodrich
Algorithms &
Data Structures
Statistics
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 17
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 18
Figure from
Carter Butts
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 19
Statistical Modeling of Network Data
• Statistics = principled approach for inference from data
– Basis for optimal prediction
• querying = computation of conditional probabilities/expectation
– Principles for handling noisy measurements
• e.g., noisy edge observation process
– Integration of different sources of information
• e.g., combining edge information with node covariates
– Quantification of uncertainty
• e.g., which model is a better explanation of the data
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 20
Limitations of Existing Methods
• Network data over time
– Relatively few statistical models for dynamic network data
• Heterogeneous data
– e.g., few techniques for incorporating text, spatial
information, etc, into network models
• Computational tractability
– Many network modeling algorithms scale exponentially in
the number of nodes N
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 21
Example
• G = {V, E}
V = set of N nodes
E = set of directed binary edges
• Exponential random graph model (ERGM)
P(G | q) = f( G ; q ) / normalization constant
The normalization constant = sum over all possible graphs
How many graphs?
2
N(N-1)
e.g., N = 20, we have 2380 ~ 1038 graphs to sum over
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 22
Key Themes of our MURI Project
• Foundational research on new statistical estimation
techniques for network data
– e.g., principles of modeling with missing data
• New algorithms for heterogeneous network data
– Incorporating time, space, text, other covariates
• Faster algorithms
– E.g., approximation methods for very large data sets
• Software
– Make network inference software publicly-available (in R)
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 23
Key Themes of our MURI Project
Fast
Algorithms
Statistical
Methods
Richer models
Large
Heterogeneous
Data Sets
Software
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 24
Tasks
A: Fast network estimation algorithms
Eppstein, Butts
B: Spatial representations and network data
Goodrich, Eppstein, Mount
C: Advanced network estimation techniques
Handcock, Hunter
D: Scalable methods for relational events
Butts
E: Network models with text data
Smyth
F: Software for network inference and prediction
Hunter
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 25
Task A: Fast Network Estimation Algorithms
Investigators: Eppstein, Butts
• Problem:
– Statistical inference algorithms can be slow because of repeated
computation of various statistics on graphs
• Goal
– Leverage ideas from computational graph algorithms to enable
much faster computation – also enabling computation of more
complex and realistic statistics
• Projects
– Dynamic graph methods for change-score computation
– Rapid subgraph automorphism detection for feature counting
– Dynamic connectivity
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 26
Task B: Spatial Representations and Network Data
Investigators: Goodrich, Eppstein, Mount
• Problem:
–
Spatial representations of network data can be quite useful (both
latent embeddings and actual spatial information) but current
statistical modeling algorithms scale poorly
• Goal
–
Build on recent efficient geometric data indexing techniques in
computer science to develop much faster and efficient algorithms
• Projects
– Improved algorithms for latent-space embeddings
– Fast implementations for high-dimensional latent space models
– Techniques for integrating actual and latent space geometry
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 27
Task C: Advanced Estimation Techniques
Investigators: Handcock, Hunter
• Problem:
–
Current statistical network inference models often make unrealistic
assumptions, e.g.,
• Assume complete (non-missing) data
• Assume that exact computation is possible
• Goal
–
Develop new theories and techniques that relax these assumptions,
i.e., methods for handing missing data and techniques for
approximate inference
• Projects
– Inference with partially observed network data
– Approximation methods
• Approximate likelihood techniques
• Approximate MCMC algorithms
– Will leverage new techniques developed in Tasks A and B
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 28
Figure from Dave Hunter, Penn State
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 29
Task D: Scalable Temporal Models
Investigator: Butts
• Problem:
–
Few statistical methods for modeling temporal sequences of
events among a network of actors
• Goal
–
Develop new statistical relational event models to handle an
evolving set of events over time in a network context
• Projects
–
–
–
–
–
Specification of relational event statistics
Rapid likelihood computation for relational event models
Predictive event system queries
Interventions, forecasting, and “network steering”
Can build on ideas from Tasks A, B, C
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 30
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 31
Task E: Network Models and Text Data
Investigator: Smyth
• Problem:
–
Lack of statistical techniques that can combine network and text
data within a single framework (e.g., email communication)
• Goal
–
Leverage recent advances in both statistical text mining and
statistical network modeling to create new combined models
• Projects
–
–
–
–
–
Latent variable models for text and network data
Text as exogenous data for statistical network models
Modeling of text and network data over time
Fast algorithms for statistical modeling of text/networks
Can build on ideas from Tasks A, B, C and D
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 32
Network of email communication
patterns in a corporate research lab
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 33
Task F: Software for Network Inference and Prediction
Investigator: Hunter
•
Goal
– Disseminate algorithms and software to research and practitioner
communities
• How?
– By incorporating our new algorithms into the R statistical package
– R = open source language for stat computing/graphics
– MURI team has significant prior experience with developing
statistical network modeling packages in R
•
•
•
•
network (Butts et al, 2007)
latentnet (Handcock et al, 2004)
ergm (Handcock et al, 2003)
sna (Butts, 2000)
• Will integrate algorithms and techniques from earlier tasks
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 34
Data Sets
• Traditional social network data sets
• “Next generation” data sets
– Dynamic network data
• E.g., WTC communications
– Network data with text
• E.g., political blogs, Enron emails
– Often much larger and richer than traditional data sets
– See afternoon talks by PhD students Lorien Jasny and
Chris DuBois
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 35
Evaluation Methods
• Traditional statistical metrics
– Log-likelihood on training data
– Model comparisons using penalized and marginal likelihood
• Predictive metrics
– E.g., for dynamic networks, prediction of edge and node
properties “out of sample”, and assessment of the accuracy of
these predictions
• Classification accuracy, precision-recall (ROC), etc
• Computational metrics
– Worst and average-case analysis
– Empirical evaluations of computation time
– Trade-offs of statistical/predictive accuracy with computation time
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 36
Summary
• Statistical modeling is a key approach for quantitative analysis
and prediction using network data
• Existing statistical network modeling techniques are
potentially very powerful
– But are currently computationally limited to small networks
• Leverage ideas from computer science to extend the “reach”
of statistical network modeling to larger networks
• Benefits
– Computationally tractable modeling of much larger networks
– More sophisticated representations for network models
– New applications of statistical network modeling
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 37
Agenda for MURI Kickoff Meeting at UC Irvine
November 18th 2008
Location: UC Irvine, Bren Hall, Room 4011
MORNING SESSION
8:30 Arrive, continental breakfast available
9:00 Introductions and overview of MURI proposal
Padhraic Smyth (UCI)
9:30 Research and application challenges from ONR's perspective
Martin Kruger (ONR, MURI Program Manager)
10:00 Brief discussion/Q&A session between ONR representatives and PIs
P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 38