All the keynotes will take place as free virtual events on the blackboard collaborate platform at the link :

KEYNOTE 1: Pascal Degenne

Wednesday, october 14, 16:00 – 17:00

Pascal Degenne is a researcher at Cirad. He is a member of the TETIS (Territoire Environnement Télédétection et Information Spatial) joint reasearch unit based at Montpellier. During the last 15 years he been working on spatial dynamics modelling and simulation, how to develop appropriate tools and how to use them on a broad range of landscape dynamics situations.

Title: Landscape dynamics simulation and interaction graphs: an illustrated overview of our experiments.
A little more than 10 years ago, a few of us decided to build a spatial dynamics modelling tool offering a wider capability of expression than what was available at that time. It lead us to use "Interaction graphs" as a central modelling concept, and the tool itself took the form of a domain specific programming language named Ocelet. This talk is about what happened next:  how we built models based on interaction graphs to address a variety of landscape dynamics issues. Even though the interaction graph concept is a low level one, it can take several forms that can be combined with each other to deal with some common issues of lanscape models : different forms of representation of space, multi-scale processes, or more generally the need to integrate in one same model different sources of knowledge. A few lessons learned during the last decade spent modelling landscape dynamics will also be exposed.

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 KEYNOTE 2: Matteo Magnani

Thursday, october 15, 9:30 – 10:30

 Matteo Magnani is associate professor at the Department of Information Technology, Uppsala University, where he directs the Uppsala Information Laboratory (http://infolab.it.uu.se) and the International Master’s Programme in Data Science. His current research lies at the intersection between Data Mining, Social Network Analysis and Network Science, and is aimed at developing models, algorithms, methodologies and software to analyse human-generated (online) data. During the last ten years he has contributed to the field of multilayer networks, and his results in this field are described in his book “Multilayer social networks”, in several peer-reviewed publications, and implemented in the multinet software library (http://multilayer.it.uu.se/software.html).

Title: Multilayer networks meet data engineering: from property graphs to online information networks.
The analysis of complex online information requires the availability of rich data representation models and data analysis algorithms, for example multilayer networks and multilayer network clustering algorithms. At the same time, data analysis processes often require the ability to store “raw" data not yet prepared to perform a specific type of analysis and the capability to efficiently transform (or query) the raw data. This is for example important when performing interactive and visual data analysis, where several data dimensions, data subsets and aggregations have to be computed dynamically to explore different views over the data. In this talk I will present an extension of the multilayer network model providing data manipulation functionality. This extension is based on the concept of data cube, often used in data warehousing and data mining. I will conclude the presentation showing how to express a process to identify online conversations about specific topics in social media as a combination of cubes and cube operators.

Video