VOCAL 2024
Abstract Submission

126. Complex network approximate symmetries motivated by brain studies

Invited abstract in session TE-4: OR applications, stream contributed papers.

Thursday, 16:45 - 18:15
Room: C105

Authors (first author is the speaker)

1. David Hartman
Department of Complex Systems, The Institute of Computer Science of the Czech Academy of Sciences
2. Anna Pidnebesna
The Institute of Computer Science of the Czech Academy of Sciences
3. Aneta Pokorna
The Institute of Computer Science of the Czech Academy of Sciences
4. Jaroslav Hlinka
National Institute of Mental Health, Prague, Czech Republic

Abstract

The human brain is a complex system that is very difficult to simulate and analyze in detail. One characteristic of this system is (a)symmetry in its structure. It is even assumed that the mentioned (a)symmetry influences various functional properties of the brain. One of the modern methods for analyzing this system is represented by complex networks. These networks describe both the structural and functional connectivity of the brain and many characteristics of the system, including potential symmetry, can be inferred from their structure. Recently, it has been shown that non-trivial symmetries based on graph automorphisms exist in many complex networks. However, such symmetries do not account for uncertainty in the edges. Therefore, a relaxed alternative allowing approximate automorphisms has recently been proposed. However, the proposed method has some shortcomings. Therefore, in this paper, we propose an alternative approach using a recently proposed optimization method from the field of graph matching with a modification for variable inclusion of fixed points. In addition to the proposed method itself, we also propose a method for testing similar algorithms for approximate symmetries on suitably constructed random models motivated by, among other things, networks in the brain.

Keywords

Status: accepted

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