Computational science has entered the era of Big Data, fueled by unparalleled amounts of data coming from high-throughput technologies and electronic records collected by various sensors and communication devices. This trend is particularly visible in communication and social networks, where the gathering of complex data on human and social interactions becomes possible at a very large scale. The efficient exploitation of that data demands the development of more efficient computational methods as well as richer models to represent the salient features of the systems.
Let us take, as an illustrative example, a social system composed of agents engaging in different types of social interactions, consuming and producing goods, exchanging information, moving to satisfy their needs and meet each other, etc. Let us further assume that one has access to a data-set providing a detailed description of the different dimensions (this assumption is far from irrealistic – see SensibleDTU, Pardus, etc.). In recent years, the most common language to describe such systems has been “Network Science”, where the organization of the system is summarized in terms of static nodes and edges, and dynamics modeled by Markov processes on an underlying network. During this abstracting step, several aspects of the data have been neglected: the nature of edges, the temporal patterns of activity of nodes and edges, pathways of information, etc. It is the fate of models to put away information. As long as this information is marginal, the model is a good one, but when the simplification neglects critical information, it needs to be improved. The main purpose of this workshop is to focus on the following questions: When are simple network models sufficient and when are they not? What additional ingredients are needed to accurately model the dynamical processes? With access to more and more relational data, what are the most efficient ways to capture the structural information?
The aim of this workshop is to bring together researchers interested in extensions to the conventional network paradigm. The 2-day workshop will be organised as a 48 hour hackathon. In the fist morning, different ideas will be pitched and one idea selected by the team. During the remainder of the project, the researchers will work hand in hand on the development of algorithms and new methods. This event is a workshop, not a talkshop. To allow coordination and facilitate cooperation, we would like to invite a small number of motivated researchers, between 6-9.
Hosted in: Wallonia
Date: May 7, 2014
Event starts at: 09:00
Organiser: Renaud Lambiotte (KNOWeSCAPE)
Register for tickets at: http://knowescape.org/event/rich-models-for-complex-data/