Characterization of the clustering phase transition of a complex network embedded in a hyperbolic plane

Projecte final de Màster Oficial reaizat en col.laboració amb Universitat de Barcelona. Departament de Física Fonamental.English: If we distribute nodes homogeneously in an hyperbolic plane and connect each possible pair of nodes with a probability that depends on the hyperbolic distance among them, heterogeneous degree distributions and strong clustering emerge naturally. Both metrics are key properties observed in real complex networks but are rarely seen together in standard network models. Our model considers edges in a network as noninteracting fermions whose energies are equal to the hyperbolic distances between nodes. This interpretation allows us to use statistical mechanics methods, like the Metropolis Hastings algorithm, in order to perform numerical simulations and to get precise measurements of the network properties. In this master thesis, we focus on the study of clustering, which undergoes a phase transition at a certain critical temperature. We develop an analytical framework to obtain the critical exponents of this phase transition and compare them with numerical simulations. Finally, we check whether the Finite Size Scaling (FSS) assumption holds in this case or not

Quantifying human engagement into playful activities

Engaging in playful activities, such as playing a musical instrument, learning a language, or performing sports, is a fundamental aspect of human life. We present a quantitative empirical analysis of the engagement dynamics into playful activities. We do so by analyzing the behavior of millions of players of casual video games and discover a scaling law governing the engagement dynamics. This power-law behavior is indicative of a multiplicative (i.e., 'happy- get-happier') mechanism of engagement characterized by a set of critical exponents. We also find, depending on the critical exponents, that there is a phase transition between the standard case where all individuals eventually quit the activity and another phase where a finite fraction of individuals never abandon the activity. The behavior that we have uncovered in this work might not be restricted only to human interaction with videogames. Instead, we believe it reflects a more general and profound behavior of how humans become engaged in challenging activities with intrinsic rewards

Deciphering the global organization of clustering in real complex networks

We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles.Fil: Colomer de Simón, Pol. Universidad de Barcelona; EspañaFil: Serrano, María de Los Angeles. Universidad de Barcelona; EspañaFil: Beiro, Mariano Gastón. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; ArgentinaFil: Alvarez Hamelin, Jose Ignacio. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; ArgentinaFil: Boguñá, Marián. Universidad de Barcelona; Españ

Libro Blanco de los Sistemas Complejos Socio-tecnológicos

La Red SocioComplex está formada por la Universitat de Barcelona (coordinación), Fundación IMDEA Networks, Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-Universitat Illes Balears), Universidad de Burgos, Universidad Carlos III de Madrid, Universitat Rovira i Virgili, Universitat de València y Universidad de Zaragoza - Instituto de Biocomputación y Física de los Sistemas Complejos.Este libro blanco analiza por primera vez las principales fuerzas de la investigación española en ciencias de la complejidad en el contexto de los sistemas socio-tecnológicos.El Libro Blanco de los Sistemas Complejos Socio-tecnológicos forma parte del conjunto de acciones realizadas por la red temática SocioComplex FIS2015-71795-REDT financiada por parte del Ministerio de Economía, Industria y Competitividad – Agencia Estatal de Investigación y del Fondo Europeo de Desarrollo Regional (FEDER)

Blue Genes : Synopsis of the workshop organized by ICM-CSIC and BAU to increase engagement and collaboration for Our Ocean and Waters

7 pagesOn October 27th, 2022, the Institut de Ciències del Mar (ICM-CSIC), a marine research institution located in Barcelona, and the College of Arts & Design of Barcelona (BAU), a higher-education centre specialized in arts and design, co-organized the first Blue Genes meeting. This meeting took place virtually in a 3-hour workshop format and counted with more than 50 participants from different locations. Its main goal was to explore in a co-creative way how to reinforce and empower the engagement of people, particularly teenagers and young adults, with our Ocean and Waters and increase networking and collaboration. This meeting was the first workshop of a series of planned activities under the Blue Genes initiativePeer reviewe

The role of clustering in the stucture and function of complex networks

[eng] The study of a system from a network perspective focuses on the impact that connectivity between the elements has on the function of the system. The observation and measurement of parameters of real-world networks reveals that these systems have highly complex structures that differ from those of lattices and random graphs, and which have striking effects on their behaviour. Moreover, some common topological properties shared by networks with completely different natures have been found. This suggests the existence of common fundamental principles that determine the structure and evolution of networks. One of the most common features of real networks is the high presence of triangles or strong clustering. However, in contrast to other topological properties of real networks, little was known about the emergence of clustering and its effect on network structure and function. The reason for this was twofold. First of all, the mere presence of triangles in networks contradicts assumptions that are used almost across the board in mathematical tools that are applied in network theory, and therefore it hinders any analytical treatment. Second, there was a lack of appropriate clustered network models that allow empirical study. Therefore, clustering was the main factor that thwarted the possibility of applying network theories to real situations and became one of the most important challenges facing network science. In this thesis we studied the role played by clustering in the structure and function of complex networks. In this direction, we first analyse the clustering generated by one of the most popular random network model: the configuration model. Our results show that, contrary to common believes strong heterogeneity can be enough to generate moderate levels of clustering. Then, we studied the distribution of triangles within real networks. Interestingly enough, real networks tend to be closer to maximally random clustered graphs, although clear differences are evident. This fact have an impact on the study of clustering on network processes since it casts doubt on previous results derived from clustered network models in which triangles were organized in a very specific way. Finally, we focus on the effect of clustering on the classical bond percolation problem. Our choice was based on the direct relation that this simple process has with robustness and epidemics dynamics of networks. Our results show that clustering makes weakly heterogeneous networks more fragile to random failure of their connections and less prone to spread infected agents. However, clustering in strongly heterogeneous networks can induce a core-periphery organization in which the core and periphery percolates independently. This phenomenon, namely a multiple percolation transition, has not been observed before. In this situation clustering makes the core more robust and the periphery more fragile. Furthermore, I analytically prove that such multiple percolation transitions are possible in networks that are sufficiently weakly connected. This new scenario has very important implications for different aspects of the analysis of the percolation properties of complex networks. On the one hand, the existence of multiple critical points changes the way we need to address percolation as a critical phenomenon. We should not develop theories to find the true and unique percolation threshold, but to reveal the set of critical points and the nodes involved in each one of them. On the other hand, this new phenomenon implies that previous empirical methods for finding the percolation threshold are obsolete. The obvious incapacity to perform finite size scaling in a real finite system, together with the existence of multiple transitions, implies that no existent empirical method can be used to measure percolation thresholds.[cat] La teoria de xarxes és útil per concentrar-se en l'impacte que els patrons de interacció entre elements tenen en la funció de sistemes. La mesura i observació de xarxes reals revela que aquests aquestes tenen unes estructures complexes amb un efecte molt important en el seu comportament. Aquest fet suggereix l'existència de patrons de formació comuns que determinen l'estructura i evolució de les xarxes. Una de les propietats més comunes de les xarxes reals és l'alta presència de triangles o fort clustering. Al contrari que altres propietats topològiques, encara es desconeix l'origen de l'emergència del clustering i el seu efecte en l'estructura i funció del sistema. En primer lloc, això és degut a que la simple presència de triangles contradiu una hipòtesi molt utilitzada en la teoria de xarxes, complicant qualsevol possibilitat de un tractament analític. En segon lloc, hi ha un manca de models de xarxes amb clustering apropiats que permetin un estudi empíric. Per tant, el clustering és un dels factors més importants que dificulta la possibilitat d'aplicar els resultats de la teoria de xarxes a casos reals. En aquesta direcció en aquesta tesi comencem estudiant el clustering generat pels models de xarxes més populars. Seguidament mirem com es distribueixen els triangles en les xarxes reals. Finalment ens concentrem en l'efecte del clustering en el clàssic problema de percolació. La nostra tria es basa en la relació que aquest procés simple té amb la robustesa i la dinàmica d'epidèmies en xarxes. Anteriors estudis sobre les propietats de percolació de xarxes amb clustering són només vàlids per una estructura específica la qual mostrem que no reprodueix la organització global dels triangles present en les xarxes reals. Per tant, per respondre aquesta pregunta hem hagut de primer desenvolupar un model de xarxa amb clustering que reprodueixi la organització dels triangles de les xarxes reals. Finalment hem fet servir el nostre model per estudiar com el clustering efecte a la posició del llindar de percolació en xarxes complexes

The role of clustering in the stucture and function of complex networks

The study of a system from a network perspective focuses on the impact that connectivity between the elements has on the function of the system. The observation and measurement of parameters of real-world networks reveals that these systems have highly complex structures that differ from those of lattices and random graphs, and which have striking effects on their behaviour. Moreover, some common topological properties shared by networks with completely different natures have been found. This suggests the existence of common fundamental principles that determine the structure and evolution of networks. One of the most common features of real networks is the high presence of triangles or strong clustering. However, in contrast to other topological properties of real networks, little was known about the emergence of clustering and its effect on network structure and function. The reason for this was twofold. First of all, the mere presence of triangles in networks contradicts assumptions that are used almost across the board in mathematical tools that are applied in network theory, and therefore it hinders any analytical treatment. Second, there was a lack of appropriate clustered network models that allow empirical study. Therefore, clustering was the main factor that thwarted the possibility of applying network theories to real situations and became one of the most important challenges facing network science. In this thesis we studied the role played by clustering in the structure and function of complex networks. In this direction, we first analyse the clustering generated by one of the most popular random network model: the configuration model. Our results show that, contrary to common believes strong heterogeneity can be enough to generate moderate levels of clustering. Then, we studied the distribution of triangles within real networks. Interestingly enough, real networks tend to be closer to maximally random clustered graphs, although clear differences are evident. This fact have an impact on the study of clustering on network processes since it casts doubt on previous results derived from clustered network models in which triangles were organized in a very specific way. Finally, we focus on the effect of clustering on the classical bond percolation problem. Our choice was based on the direct relation that this simple process has with robustness and epidemics dynamics of networks. Our results show that clustering makes weakly heterogeneous networks more fragile to random failure of their connections and less prone to spread infected agents. However, clustering in strongly heterogeneous networks can induce a core-periphery organization in which the core and periphery percolates independently. This phenomenon, namely a multiple percolation transition, has not been observed before. In this situation clustering makes the core more robust and the periphery more fragile. Furthermore, I analytically prove that such multiple percolation transitions are possible in networks that are sufficiently weakly connected. This new scenario has very important implications for different aspects of the analysis of the percolation properties of complex networks. On the one hand, the existence of multiple critical points changes the way we need to address percolation as a critical phenomenon. We should not develop theories to find the true and unique percolation threshold, but to reveal the set of critical points and the nodes involved in each one of them. On the other hand, this new phenomenon implies that previous empirical methods for finding the percolation threshold are obsolete. The obvious incapacity to perform finite size scaling in a real finite system, together with the existence of multiple transitions, implies that no existent empirical method can be used to measure percolation thresholds.La teoria de xarxes és útil per concentrar-se en l'impacte que els patrons de interacció entre elements tenen en la funció de sistemes. La mesura i observació de xarxes reals revela que aquests aquestes tenen unes estructures complexes amb un efecte molt important en el seu comportament. Aquest fet suggereix l'existència de patrons de formació comuns que determinen l'estructura i evolució de les xarxes. Una de les propietats més comunes de les xarxes reals és l'alta presència de triangles o fort clustering. Al contrari que altres propietats topològiques, encara es desconeix l'origen de l'emergència del clustering i el seu efecte en l'estructura i funció del sistema. En primer lloc, això és degut a que la simple presència de triangles contradiu una hipòtesi molt utilitzada en la teoria de xarxes, complicant qualsevol possibilitat de un tractament analític. En segon lloc, hi ha un manca de models de xarxes amb clustering apropiats que permetin un estudi empíric. Per tant, el clustering és un dels factors més importants que dificulta la possibilitat d'aplicar els resultats de la teoria de xarxes a casos reals. En aquesta direcció en aquesta tesi comencem estudiant el clustering generat pels models de xarxes més populars. Seguidament mirem com es distribueixen els triangles en les xarxes reals. Finalment ens concentrem en l'efecte del clustering en el clàssic problema de percolació. La nostra tria es basa en la relació que aquest procés simple té amb la robustesa i la dinàmica d'epidèmies en xarxes. Anteriors estudis sobre les propietats de percolació de xarxes amb clustering són només vàlids per una estructura específica la qual mostrem que no reprodueix la organització global dels triangles present en les xarxes reals. Per tant, per respondre aquesta pregunta hem hagut de primer desenvolupar un model de xarxa amb clustering que reprodueixi la organització dels triangles de les xarxes reals. Finalment hem fet servir el nostre model per estudiar com el clustering efecte a la posició del llindar de percolació en xarxes complexes

Characterization of the clustering phase transition of a complex network embedded in a hyperbolic plane

Projecte final de Màster Oficial reaizat en col.laboració amb Universitat de Barcelona. Departament de Física Fonamental.English: If we distribute nodes homogeneously in an hyperbolic plane and connect each possible pair of nodes with a probability that depends on the hyperbolic distance among them, heterogeneous degree distributions and strong clustering emerge naturally. Both metrics are key properties observed in real complex networks but are rarely seen together in standard network models. Our model considers edges in a network as noninteracting fermions whose energies are equal to the hyperbolic distances between nodes. This interpretation allows us to use statistical mechanics methods, like the Metropolis Hastings algorithm, in order to perform numerical simulations and to get precise measurements of the network properties. In this master thesis, we focus on the study of clustering, which undergoes a phase transition at a certain critical temperature. We develop an analytical framework to obtain the critical exponents of this phase transition and compare them with numerical simulations. Finally, we check whether the Finite Size Scaling (FSS) assumption holds in this case or not

The role of clustering in the stucture and function of complex networks

The study of a system from a network perspective focuses on the impact that connectivity between the elements has on the function of the system. The observation and measurement of parameters of real-world networks reveals that these systems have highly complex structures that differ from those of lattices and random graphs, and which have striking effects on their behaviour. Moreover, some common topological properties shared by networks with completely different natures have been found. This suggests the existence of common fundamental principles that determine the structure and evolution of networks. One of the most common features of real networks is the high presence of triangles or strong clustering. However, in contrast to other topological properties of real networks, little was known about the emergence of clustering and its effect on network structure and function. The reason for this was twofold. First of all, the mere presence of triangles in networks contradicts assumptions that are used almost across the board in mathematical tools that are applied in network theory, and therefore it hinders any analytical treatment. Second, there was a lack of appropriate clustered network models that allow empirical study. Therefore, clustering was the main factor that thwarted the possibility of applying network theories to real situations and became one of the most important challenges facing network science. In this thesis we studied the role played by clustering in the structure and function of complex networks. In this direction, we first analyse the clustering generated by one of the most popular random network model: the configuration model. Our results show that, contrary to common believes strong heterogeneity can be enough to generate moderate levels of clustering. Then, we studied the distribution of triangles within real networks. Interestingly enough, real networks tend to be closer to maximally random clustered graphs, although clear differences are evident. This fact have an impact on the study of clustering on network processes since it casts doubt on previous results derived from clustered network models in which triangles were organized in a very specific way. Finally, we focus on the effect of clustering on the classical bond percolation problem. Our choice was based on the direct relation that this simple process has with robustness and epidemics dynamics of networks. Our results show that clustering makes weakly heterogeneous networks more fragile to random failure of their connections and less prone to spread infected agents. However, clustering in strongly heterogeneous networks can induce a core-periphery organization in which the core and periphery percolates independently. This phenomenon, namely a multiple percolation transition, has not been observed before. In this situation clustering makes the core more robust and the periphery more fragile. Furthermore, I analytically prove that such multiple percolation transitions are possible in networks that are sufficiently weakly connected. This new scenario has very important implications for different aspects of the analysis of the percolation properties of complex networks. On the one hand, the existence of multiple critical points changes the way we need to address percolation as a critical phenomenon. We should not develop theories to find the true and unique percolation threshold, but to reveal the set of critical points and the nodes involved in each one of them. On the other hand, this new phenomenon implies that previous empirical methods for finding the percolation threshold are obsolete. The obvious incapacity to perform finite size scaling in a real finite system, together with the existence of multiple transitions, implies that no existent empirical method can be used to measure percolation thresholds.La teoria de xarxes és útil per concentrar-se en l'impacte que els patrons de interacció entre elements tenen en la funció de sistemes. La mesura i observació de xarxes reals revela que aquests aquestes tenen unes estructures complexes amb un efecte molt important en el seu comportament. Aquest fet suggereix l'existència de patrons de formació comuns que determinen l'estructura i evolució de les xarxes. Una de les propietats més comunes de les xarxes reals és l'alta presència de triangles o fort clustering. Al contrari que altres propietats topològiques, encara es desconeix l'origen de l'emergència del clustering i el seu efecte en l'estructura i funció del sistema. En primer lloc, això és degut a que la simple presència de triangles contradiu una hipòtesi molt utilitzada en la teoria de xarxes, complicant qualsevol possibilitat de un tractament analític. En segon lloc, hi ha un manca de models de xarxes amb clustering apropiats que permetin un estudi empíric. Per tant, el clustering és un dels factors més importants que dificulta la possibilitat d'aplicar els resultats de la teoria de xarxes a casos reals. En aquesta direcció en aquesta tesi comencem estudiant el clustering generat pels models de xarxes més populars. Seguidament mirem com es distribueixen els triangles en les xarxes reals. Finalment ens concentrem en l'efecte del clustering en el clàssic problema de percolació. La nostra tria es basa en la relació que aquest procés simple té amb la robustesa i la dinàmica d'epidèmies en xarxes. Anteriors estudis sobre les propietats de percolació de xarxes amb clustering són només vàlids per una estructura específica la qual mostrem que no reprodueix la organització global dels triangles present en les xarxes reals. Per tant, per respondre aquesta pregunta hem hagut de primer desenvolupar un model de xarxa amb clustering que reprodueixi la organització dels triangles de les xarxes reals. Finalment hem fet servir el nostre model per estudiar com el clustering efecte a la posició del llindar de percolació en xarxes complexes