Clemente, Giulio Virginio (2024) The Maximum Entropy Principle for Temporal and Ecological Networks: Memory, Fluctuations and Response in Complex Systems. Advisor: Garlaschelli, Prof. Diego. Coadvisor: Caruso, Prof. Tancredi . pp. 241. [IMT PhD Thesis]
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Text (Doctoral thesis)
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Abstract
Many real systems, represented using complex networks, often exhibit intrinsic dynamism that uncovers fundamental properties. This thesis explores various aspects of such dynamism through the lens of maximum entropy formalism, presenting methodologies that effectively characterize and harness this aspect. The work is divided into two main parts. The first part develops a novel maximum entropy model to characterize memory effects and structural heterogeneity in temporal networks. This model captures the evolution of network connections over time, focusing on how nodes create and maintain links. Utilizing this model, the research uncovers topological patterns, such as community structures, emphasizing the role of memory mechanisms in encoding network properties. The second part shifts focus to ecological networks, emphasizing system fluctuations for modeling and predictive analysis. Here, maximum entropy formalism is shown to be a tool capable of constructing models that incorporate significant fluctuations in system characteristics. These models are then shown to enhance pattern detection, particularly emphasizing the ecological contexts. Finally, I discuss how this approach can be used to define a new perspective on the diversity-stability debate by linking entropy with system stability and demonstrating how, through the Fluctuation Response Relation, properly characterized fluctuations can predict systems’ response to perturbations.
| Item Type: | IMT PhD Thesis |
|---|---|
| Subjects: | H Social Sciences > HB Economic Theory |
| PhD Course: | Economics, Networks and Business Analytics |
| Identification Number: | https://doi.org/10.13118/imtlucca/e-theses/427/ |
| NBN Number: | urn:nbn:it:imtlucca-30580 |
| Date Deposited: | 25 Sep 2024 13:10 |
| URI: | http://e-theses.imtlucca.it/id/eprint/427 |
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