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Latent Feature Models for Network Data over Time Jimmy Foulds Advisor: Padhraic Smyth

Latent Feature Models for Network Data over Time Jimmy Foulds Advisor: Padhraic Smyth (Thanks also to Arthur Asuncion and Chris Dubois). Overview. The task Prior work – Miller, Van Gael, Indian Buffet Processes The DRIFT model Inference Preliminary results Future work. The Task.

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Latent Feature Models for Network Data over Time Jimmy Foulds Advisor: Padhraic Smyth

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  1. Latent Feature Models for Network Data over Time Jimmy Foulds Advisor: Padhraic Smyth (Thanks also to Arthur Asuncion and Chris Dubois)

  2. Overview • The task • Prior work – Miller, Van Gael, Indian Buffet Processes • The DRIFT model • Inference • Preliminary results • Future work

  3. The Task • Modeling Dynamic (time-varying) Social Networks • Interested in prediction • Model interpretation for sociological understanding • Continuous time relational events versus panel data?

  4. Applications • Predicting Email Communications

  5. Applications • Predicting Paper Co-authorship • NIPS data

  6. Prior Work • Erdos-Renyi Models are “pseudo-dynamic” • Continuous Markov Process Models (Snijders 2006) • The network stochastically optimizes ERGM likelihood function • Dynamic Latent Space Model (Sarkar & Moore, 2005) • Each node (actor) is associated with a point in a low dimensional space (Raftery et al. 2002). Link probability is a function of distance between points • Gaussian jumps in latent space in each timestep

  7. Prior Work • Nonparametric Latent Feature Relational Model (Miller et al. 2009) • Each actor is associated with an unbounded sparse vector of binary latent features, generated from an Indian Buffet Process prior • The probability of a link between two actors is a function of the latent features of those actors (and additional covariates)

  8. Prior Work • Nonparametric Latent Feature Relational Model (Miller et al. 2009) generative process: • Z ~ IBP(a) • Wkk' ~ N(0,sw) • Yij ~ s(ZiWZjT + covariate terms) • A kind of blockmodel with overlapping classes

  9. How to Make this Model Dynamic For Longitudinal Data? • We would like the Zs to change over time, modeling changing interests, community memberships, … • Want to maintain sparsity property, but model persistence, generation of new features, ...

  10. Infinite Factorial Hidden Markov Models (Van Gael et al., 2010) • A variant of the IBP • A probability distribution over a potentially infinite number of binary Markov chains • Sparsity: At each timestep, introduce new features using the IBP distribution • Persistence: A coin flip determines whether each feature persists to the next timestep • Hidden Markov structure: the latent features are hidden but we observe something at each timestep.

  11. DRIFT: the Dynamic Relational Infinite FeaTure Model • The iFHMM models the evolution of one actor's features over time • We use an iFHMM for each actor, but share the transition probabilities • Observed graphs generated via (Miller et al. 2009)'s latent feature model Yij ~ s(ZiWZjT +...)

  12. DRIFT: the Dynamic Relational Infinite FeaTure Model

  13. Inference • Markov chain Monte Carlo inference • Use “slice sampling” trick with the stick-breaking construction of the IBP to effectively truncate num features but still perform exact inference • Blocked Gibbs sampling on the other variables • Forward-backward dynamic programming on each actor's feature chain • Metropolis-Hastings updates for W's since non-conjugate

  14. Group DRIFT • Clustering to reduce the number of chains • Each actor has hidden class variable c < C < N • The chains of infinite binary feature vectors are associated with classes rather than actors • Allows us to scale up to large numbers of actors • Clustering may be interpretable

  15. Group DRIFT i=1:C Cn β=1/C • Inference for a, b, exactly the same • Inference for z’s similar: • Slightly different “emission” probability • Run forward-backward sampler on M*C chains rather than M*N chains Inference for c’s (actor’s assignment to specific chain) is easy too Inference for W is similar (slightly different likelihood). Note we must now assume that the diagonal of W can be non-zero. n=1:N

  16. Preliminary Experimental Results (Synthetic Data)

  17. Preliminary Experimental Results (Synthetic Data)

  18. Preliminary Experimental Results (Synthetic Data)

  19. Future work • Extension to Continuous Time • It's easy to use IBP latent factor model as a covariate in Relational Event Model (Butts 2008) • How to model the Zs changing over time for continuous data?

  20. Thanks for Listening!

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