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Network Inference and Reconstruction : We will provide a concise introduction to principled techniques for the detection of patterns and formation mechanisms from network data, grounded in Bayesian statistical inference and information theory. We will learn how to distinguish structure from statistical noise, how to perform uncertainty quantification, and to compare different generative models. We will also see how these ideas can be extended to reconstruct networks; both when their structure is measured directly but unreliably, and also when the structure is not measured directly at all, and needs to be uncovered from time-series or other indirect non-network data.

Longitudinal language processing from user generated content: In most of the tasks and models that we have made great progress within NLP in recent years, there isn't a notion of time. However many tasks are sensitive to changes and temporality in real world data, especially when pertaining to individuals, their behaviour and their evolution over time, as is the case with user generated content and social media data. I will introduce a programme of work on longitudinal natural language processing. This consists in developing natural language processing methods to: (1) represent individuals over time from their language and other heterogenous content (2) capture changes in individuals' behaviour over time (3) generate and evaluate synthetic data from individuals' content over time (4) summarise the progress of an individual over time, incorporating information about changes.

1) Network Inference and Reconstruction
Tiago DE PAULA PEIXOTO
CEU Vienna, Austria

2) Longitudinal language processing from user generated content
Maria LIAKATA
Queen Mary University of London, UK

One Dinner Banquet ticket is included in the registration. Extra tickets are requested for guests.