Prof Padhraic Smyth
Mon 06 Jul 2015, 10:15 - 11:15
Informatics Forum (IF-4.31/4.33)

If you have a question about this talk, please contact: Brittany Bovenzi (bbovenzi)

Speaker: Prof Padhraic Smyth, Department of Computer Science, University of California, Irvine

Title: Latent Variable and Temporal Event Models for Network Data


Date: Monday 6 July 2015

Time: 10:15-11:15am

Location: Room 4.31/4.33, Informatics Forum, University of Edinburgh

Chair: Chris Williams, Director of the EPSRC CDT in Data Science, University of Edinburgh



Social network analysis has a long history in the social sciences, often with a focus on relatively small survey-based data sets. In the past decade, driven by the ease of collecting network information in an automated manner (e.g., for social media and phone networks)  there has been significant interest in developing machine learning techniques for network data. This has led to an increased emphasis on topics that were traditionally beyond the scope of traditional social network analysis:  integration of non-network data (such as text), scalability to large networks, and predictive evaluation. In this talk we will discuss recent progress on two classes of models in this general context: latent variable models for static networks and relational event models for temporal networks. We will review the representational capabilities of these models from a generative perspective, discuss some of the challenges of parameter estimation that arise in this context, and emphasize the role of predictive evaluation for network modeling. The talk will conclude with a brief discussion of future directions in this general area.
Based on joint work with Chris DuBois, Carter Butts, and Jimmy Foulds.


Further information regarding Padhraic Smyth:


The CDT in Data Science Seminar series, held by the EPSRC CDT in Data Science at the University of Edinburgh, invites speakers from academia to discuss research in data science. The seminars are open to all and will be of particular interest to students, academic and technical staff.