English: The presentation by Wojciech Szpankowski, from Purdue University, is part of the Pathways to the 2023 IHP thematic project Random Processes in the Brain.

In this seminar, Szpankowski talks how the Shannon's information theory has served as a bedrock for advances in communication and storage systems over the past five decades. However, this theory does not handle well higher order structures (e.g., graphs, geometric structures), temporal aspects (e.g., real-time considerations), or semantics. We argue that these are essential aspects of data and information that underly a broad class of current and emerging data science applications. In this talk, we present some recent results on structural and temporal information. We first show how to extract temporal information in dynamic networks (arrival of nodes) from its structure (unlabeled graphs). We then proceed to establish fundamental limits on information content for some data structures, and present asymptotically optimal lossless compression algorithms achieving these limits for various graph models.

You can learn more about the Pathways to the 2023 IHP thematic project Random Processes in the Brain on this link: https://rpbihp.numec.prp.usp.br/index.php/Pathways_to_the_2023_IHP_thematic_program_Random_Processes_in_the_Brain