At least one author of each accepted paper must be registered by the author registration deadline in order for that contribution to appear in the proceedings and to be scheduled for presentation.

Registration deadline: October 15, 2026

At least one author of each Extended Abstract must be registered by the author registration deadlinein order for that contribution to appear in the book of abstract and to be scheduled for presentation.

Submissio Registration deadline: October 15, 2026

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Authors with 3 accepted submissions must register twice
1 registration for one submission and one extra submission
1 registration for the third submission

Extra abstract registration rate is applicable only once for each full registration. Authors with 3 accepted submissions must register two times
1 registration for one submission and one extra submission
1 registration for the third submission

Extra Page Fee (max 2 pages over the 12 pages limit)

Other Author of a contribution or attendee with no contribution.

Student fee is for non-author attendees. It is not available for authors of papers and abstract

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.