Timezone: »
The development of statistical models for continuous-time longitudinal network data is of increasing interest in machine learning and social science. Leveraging ideas from survival and event history analysis, we introduce a continuous-time regression modeling framework for network event data that can incorporate both time-dependent network statistics and time-varying regression coefficients. We also develop an efficient inference scheme that allows our approach to scale to large networks. On synthetic and real-world data, empirical results demonstrate that the proposed inference approach can accurately estimate the coefficients of the regression model, which is useful for interpreting the evolution of the network; furthermore, the learned model has systematically better predictive performance compared to standard baseline methods.
Author Information
Duy Q Vu (Pennsylvania State University)
Arthur Asuncion (University of California, Irvine)
David Hunter (Penn State University)
Padhraic Smyth (University of California, Irvine)
Related Events (a corresponding poster, oral, or spotlight)
-
2011 Oral: Continuous-Time Regression Models for Longitudinal Networks »
Thu. Dec 15th 09:20 -- 09:40 AM Room
More from the Same Authors
-
2022 : Probabilistic Querying of Continuous-Time Sequential Events »
Alex Boyd · Yuxin Chang · Stephan Mandt · Padhraic Smyth -
2023 Poster: Zero-Shot Batch-Level Anomaly Detection »
Aodong Li · Chen Qiu · Marius Kloft · Padhraic Smyth · Maja Rudolph · Stephan Mandt -
2022 Poster: Predictive Querying for Autoregressive Neural Sequence Models »
Alex Boyd · Samuel Showalter · Stephan Mandt · Padhraic Smyth -
2021 Poster: Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning »
Aodong Li · Alex Boyd · Padhraic Smyth · Stephan Mandt -
2021 Poster: Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration »
Gavin Kerrigan · Padhraic Smyth · Mark Steyvers -
2020 Poster: Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference »
Disi Ji · Padhraic Smyth · Mark Steyvers -
2020 Poster: User-Dependent Neural Sequence Models for Continuous-Time Event Data »
Alex Boyd · Robert Bamler · Stephan Mandt · Padhraic Smyth -
2017 : Coffee break and Poster Session II »
Mohamed Kane · Albert Haque · Vagelis Papalexakis · John Guibas · Peter Li · Carlos Arias · Eric Nalisnick · Padhraic Smyth · Frank Rudzicz · Xia Zhu · Theodore Willke · Noemie Elhadad · Hans Raffauf · Harini Suresh · Paroma Varma · Yisong Yue · Ognjen (Oggi) Rudovic · Luca Foschini · Syed Rameel Ahmad · Hasham ul Haq · Valerio Maggio · Giuseppe Jurman · Sonali Parbhoo · Pouya Bashivan · Jyoti Islam · Mirco Musolesi · Chris Wu · Alexander Ratner · Jared Dunnmon · Cristóbal Esteban · Aram Galstyan · Greg Ver Steeg · Hrant Khachatrian · Marc Górriz · Mihaela van der Schaar · Anton Nemchenko · Manasi Patwardhan · Tanay Tandon -
2016 Workshop: Towards an Artificial Intelligence for Data Science »
Charles Sutton · James Geddes · Zoubin Ghahramani · Padhraic Smyth · Chris Williams -
2012 Workshop: Algorithmic and Statistical Approaches for Large Social Network Data Sets »
Michael Goodrich · Pavel N Krivitsky · David M Mount · Christopher DuBois · Padhraic Smyth -
2010 Spotlight: Learning concept graphs from text with stick-breaking priors »
America Chambers · Padhraic Smyth · Mark Steyvers -
2010 Poster: Learning concept graphs from text with stick-breaking priors »
America Chambers · Padhraic Smyth · Mark Steyvers -
2009 Poster: Particle-based Variational Inference for Continuous Systems »
Alexander Ihler · Andrew Frank · Padhraic Smyth -
2008 Poster: Asynchronous Distributed Learning of Topic Models »
Arthur Asuncion · Padhraic Smyth · Max Welling -
2007 Spotlight: Distributed Inference for Latent Dirichlet Allocation »
David Newman · Arthur Asuncion · Padhraic Smyth · Max Welling -
2007 Poster: Distributed Inference for Latent Dirichlet Allocation »
David Newman · Arthur Asuncion · Padhraic Smyth · Max Welling -
2006 Poster: Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model »
Chaitanya Chemudugunta · Padhraic Smyth · Mark Steyvers -
2006 Poster: Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models »
Alexander Ihler · Padhraic Smyth -
2006 Poster: Hierarchical Dirichlet Processes with Random Effects »
Seyoung Kim · Padhraic Smyth