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4185. Evaluating approximate symmetries in complex brain networks
Invited abstract in session TC-52: Exact methods in combinatorial optimization (Contributed), stream Combinatorial Optimization.
Tuesday, 12:30-14:00Room: 8003 (building: 202)
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
Complex networks representing structural properties of natural systems exhibit characteristics such as small-world or scale-free. However, the symmetry of such networks has not been studied much until recently, although some networks, such as the human brain, have symmetries directly embedded in their structure, or such symmetry is an indicator of their specific state. In a recent study, however, it was shown that non-trivial symmetries based on graph automorphisms exist in general complex networks representing real-world systems. Nevertheless, due to the uncertainty of determining the edges of such a network, it is necessary to consider approximate symmetries, as was shown in a recent study by Liu 2020, where the image of the resulting automorphism may show inconsistencies for several pairs. However, the proposed optimization method has shortcomings, such as, for example, the instability of the results, relatively small radius of search, and not considering fixed points. In this paper, we consider adopting the method recently developed for treating the Graph Matching Problem and propose an alternative method that addresses some of the shortcomings of the approach mentioned above. In addition, we propose a scheme for testing the approximate symmetry methods using different random networks reflecting the approximate symmetry in the network representing the complex networks with emphasis on the human brain.
Keywords
- Graphs and Networks
- Combinatorial Optimization
- Computational Biology, Bioinformatics and Medicine
Status: accepted
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