Marchese, Emiliano (2022) Optimizing complex networks models. Advisor: Caldarelli, Prof. Guido. Coadvisor: Squartini, Prof. Tiziano . pp. 201. [IMT PhD Thesis]
![[img]](http://e-theses.imtlucca.it/style/images/fileicons/text.png) |
Text (Doctoral thesis)
Marchese_phdthesis.pdf - Published Version Available under License Creative Commons Attribution Non-commercial Share Alike. Download (42MB) |
Abstract
Analyzing real-world networks ultimately amounts at com- paring their empirical properties with the outcome of a proper, statistical model. The far most common, and most useful, approach to define benchmarks rests upon the so-called canonical formalism of statistical mechanics which has led to the definition of the broad class of models known as Exponential Random Graphs (ERGs). Generally speaking, employing a model of this family boils down at maximizing a likelihood function that embodies the available information about a certain system, hence constituting the desired benchmark. Although powerful, the aforementioned models cannot be solved analytically, whence the need to rest upon numerical recipes for their optimization. Generally speaking, this is a hard task, since real-world networks can be enormous in size (for example, consisting of billions of nodes and links), hence requiring models with ‘many’ parameters (say, of the same order of magnitude of the number of nodes). This evidence calls for optimization algorithms which are both fast and scalable: the collection of works constituting the present thesis represents an attempt to fill this gap. Chapter 1 provides a quick introduction to the topic. Chapter 2 deals specifically with ERGs: after reviewing the basic concepts constituting the pillars upon which such a framework is based, we will discuss several instances of it and three different numerical techniques for their optimization. Chapter 3, instead, focuses on the detection of mesoscale structures and, in particular, on the formalism based upon surprise: as the latter allows any partition of nodes to be assigned a p-value, detecting a specific, mesoscale structural organization can be understood as the problem of finding the corresponding, most significant partition - i.e. an optimization problem whose score function is, precisely, surprise. Finally, chapter 4 deals with the application of a couple of ERGs and of the surprise-based formalism to cryptocurrencies (specifically, Bitcoin).
| Item Type: | IMT PhD Thesis |
|---|---|
| Subjects: | H Social Sciences > H Social Sciences (General) H Social Sciences > HA Statistics H Social Sciences > HB Economic Theory |
| PhD Course: | Economics, Networks and Business Analytics |
| Identification Number: | https://doi.org/10.13118/imtlucca/e-theses/356 |
| NBN Number: | urn:nbn:it:imtlucca-28349 |
| Date Deposited: | 11 Jul 2022 08:16 |
| URI: | http://e-theses.imtlucca.it/id/eprint/356 |
Actions (login required, only for staff repository)
 |
View Item |
