"Structural and Temporal Information" is the eighth seminar in the series Pathways to the 2023 IHP thematic project Random Processes in the Brain.
Title: Structural and Temporal Information
Date: Tuesday, November 8, 2022 at 10:00 GMT-3
Speaker: Wojciech Szpankowski
Affiliation: Purdue University
Abstract: 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.
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