1. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 1 Scalable Methods for the Analysis of Network-Based Data MURI Project: University of California, Irvine Principal Investigator: Padhraic Smyth Kick-off Meeting November 18 th 2008

2. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 2 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 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 Butts

3. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 3 Outline Introductions Review today’s agenda –Schedule of talks –Logistics Overview of our MURI research project –Themes and goals –Tasks

4. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 4 MURI Investigators Carter Butts UCI Michael Goodrich UCI Dave Hunter Penn State David Eppstein UCI Padhraic Smyth UCI Mark Handcock U Washington Dave Mount U Maryland

5. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 5 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

6. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 6 Outline Introductions Review today’s agenda –Schedule of talks –Logistics Overview of our MURI research project –Themes and goals –Tasks

7. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 7 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

8. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 8 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)

9. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 9 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)

10. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 10 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)

11. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 11 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

12. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 12 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

13. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 13 Outline Introductions Review today’s agenda –Schedule of talks –Logistics Overview of our MURI research project –Themes and goals –Tasks

14. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 14 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

15. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 15 A Small Social Network Butts Smyth Hunter Handcock Mount Goodrich Eppstein

16. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 16 A Small Social Network Statistics Smyth Hunter Handcock Mount Goodrich Eppstein Butts

17. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 17 A Small Social Network Statistics Smyth Hunter Handcock Mount Goodrich Eppstein Butts Algorithms & Data Structures

18. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 18

19. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 19 Figure from Carter Butts

20. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 20 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

21. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 21 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

22. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 22 Example G = {V, E} V = set of N nodes E = set of directed binary edges Exponential random graph model (ERGM) P(G |  ) = f( G ;  ) / normalization constant The normalization constant = sum over all possible graphs How many graphs? 2 N(N-1) e.g., N = 20, we have 2 380 ~ 10 38 graphs to sum over

23. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 23 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)

24. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 24 Key Themes of our MURI Project Fast Algorithms Statistical Methods Richer models Software Large Heterogeneous Data Sets

25. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 25 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

26. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 26 Task A: Fast Network Estimation Algorithms 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 Investigators: Eppstein, Butts

27. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 27 Task B: Spatial Representations and Network Data 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 Investigators: Goodrich, Eppstein, Mount

28. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 28 Task C: Advanced Estimation Techniques 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 Investigators: Handcock, Hunter

29. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 29 Figure from Dave Hunter, Penn State

30. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 30 Task D: Scalable Temporal Models 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 Investigator: Butts

31. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 31

32. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 32 Task E: Network Models and Text Data 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 Investigator: Smyth

33. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 33 Network of email communication patterns in a corporate research lab

34. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 34 Task F: Software for Network Inference and Prediction 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 Investigator: Hunter

35. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 35 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

36. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 36 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

37. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 37 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

38. P. Smyth: Networks MURI Kickoff Meeting, Nov 18, 2008: 38 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