Smeared phase transitions in percolation on real complex networks

Percolation on complex networks is used both as a model for dynamics on networks, such as network robustness or epidemic spreading, and as a benchmark for our models of networks, where our ability to predict percolation measures our ability to describe the networks themselves. In many applications, correctly identifying the phase transition of percolation on real-world networks is of critical importance. Unfortunately, this phase transition is obfuscated by the finite size of real systems, making it hard to distinguish finite size effects from the inaccuracy of a given approach that fails to capture important structural features. Here, we borrow the perspective of smeared phase transitions and argue that many observed discrepancies are due to the complex structure of real networks rather than to finite size effects only. In fact, several real networks often used as benchmarks feature a smeared phase transition where inhomogeneities in the topological distribution of the order parameter do not vanish in the thermodynamic limit. We find that these smeared transitions are sometimes better described as sequential phase transitions within correlated subsystems. Our results shed light not only on the nature of the percolation transition in complex systems, but also provide two important insights on the numerical and analytical tools we use to study them. First, we propose a measure of local susceptibility to better detect both clean and smeared phase transitions by looking at the topological variability of the order parameter. Second, we highlight a shortcoming in state-of-the-art analytical approaches such as message passing, which can detect smeared transitions but not characterize their nature.Comment: 10 pages, 8 figure

On the growth and structure of social systems following preferential attachment

L’inégalité est une caractéristique notoire des systèmes sociaux. Dans cette thèse, nous nous attarderons à la distribution et à la structure de la répartition de leurs ressources et activités. Dans ce contexte, leurs extrêmes iniquités tendent à suivre une propriété universelle, l’indépendance d’échelle, qui se manifeste par l’absence d’échelle caractéristique. En physique, les organisations indépendantes d’échelle sont bien connues en théorie des transitions de phase dans laquelle on les observe à des points critiques précis. Ceci suggère que des mécanismes bien définis sont potentiellement responsables de l’indépendance d’échelle des systèmes sociaux. Cette analogie est donc au coeur de cette thèse, dont le but est d’aborder ce problème de nature multidisciplinaire avec les outils de la physique statistique. En premier lieu, nous montrons qu’un système dont la distribution de ressource croît vers l’indépendance d’échelle se trouve assujetti à deux contraintes temporelles particulières. La première est l’attachement préférentiel, impliquant que les riches s’enrichissent. La seconde est une forme générale de comportement d’échelle à délai entre la croissance de la population et celle de la ressource. Ces contraintes dictent un comportement si précis qu’une description instantanée d’une distribution est suffisante pour reconstruire son évolution temporelle et prédire ses états futurs. Nous validons notre approche au moyen de diverses sphères d’activités humaines dont les activités des utilisateurs d’une page web, des relations sexuelles dans une agence d’escorte, ainsi que la productivité d’artistes et de scientifiques. En second lieu, nous élargissons notre théorie pour considérer la structure résultante de ces activités. Nous appliquons ainsi nos travaux à la théorie des réseaux complexes pour décrire la structure des connexions propre aux systèmes sociaux. Nous proposons qu’une importante classe de systèmes complexes peut être modélisée par une construction hiérarchique de niveaux d’organisation suivant notre théorie d’attachement préférentiel. Nous montrons comment les réseaux complexes peuvent être interprétés comme une projection de ce modèle de laquelle émerge naturellement non seulement leur indépendance d’échelle, mais aussi leur modularité, leur structure hiérarchique, leurs caractéristiques fractales et leur navigabilité. Nos résultats suggèrent que les réseaux sociaux peuvent être relativement simples, et que leur complexité apparente est largement une réflexion de la structure hiérarchique complexe de notre monde.Social systems are notoriously unfair. In this thesis, we focus on the distribution and structure of shared resources and activities. Through this lens, their extreme inequalities tend to roughly follow a universal pattern known as scale independence which manifests itself through the absence of a characteristic scale. In physical systems, scale-independent organizations are known to occur at critical points in phase transition theory. The position of this critical behaviour being very specific, it is reasonable to expect that the distribution of a social resource might also imply specific mechanisms. This analogy is the basis of this work, whose goal is to apply tools of statistical physics to varied social activities. As a first step, we show that a system whose resource distribution is growing towards scale independence is subject to two constraints. The first is the well-known preferential attachment principle, a mathematical principle roughly stating that the rich get richer. The second is a new general form of delayed temporal scaling between the population size and the amount of available resource. These constraints pave a precise evolution path, such that even an instantaneous snapshot of a distribution is enough to reconstruct its temporal evolution and predict its future states. We validate our approach on diverse spheres of human activities ranging from scientific and artistic productivity, to sexual relations and online traffic. We then broaden our framework to not only focus on resource distribution, but to also consider the resulting structure. We thus apply our framework to the theory of complex networks which describes the connectivity structure of social, technological or biological systems. In so doing, we propose that an important class of complex systems can be modelled as a construction of potentially infinitely many levels of organization all following the same universal growth principle known as preferential attachment. We show how real complex networks can be interpreted as a projection of our model, from which naturally emerge not only their scale independence, but also their clustering or modularity, their hierarchy, their fractality and their navigability. Our results suggest that social networks can be quite simple, and that the apparent complexity of their structure is largely a reflection of the complex hierarchical nature of our world

Modeling the dynamical interaction between epidemics on overlay networks

Epidemics seldom occur as isolated phenomena. Typically, two or more viral agents spread within the same host population and may interact dynamically with each other. We present a general model where two viral agents interact via an immunity mechanism as they propagate simultaneously on two networks connecting the same set of nodes. Exploiting a correspondence between the propagation dynamics and a dynamical process performing progressive network generation, we develop an analytic approach that accurately captures the dynamical interaction between epidemics on overlay networks. The formalism allows for overlay networks with arbitrary joint degree distribution and overlap. To illustrate the versatility of our approach, we consider a hypothetical delayed intervention scenario in which an immunizing agent is disseminated in a host population to hinder the propagation of an undesirable agent (e.g. the spread of preventive information in the context of an emerging infectious disease).Comment: Accepted for publication in Phys. Rev. E. 15 pages, 7 figure

Percolation on random networks with arbitrary k-core structure

The k-core decomposition of a network has thus far mainly served as a powerful tool for the empirical study of complex networks. We now propose its explicit integration in a theoretical model. We introduce a Hard-core Random Network model that generates maximally random networks with arbitrary degree distribution and arbitrary k-core structure. We then solve exactly the bond percolation problem on the HRN model and produce fast and precise analytical estimates for the corresponding real networks. Extensive comparison with selected databases reveals that our approach performs better than existing models, while requiring less input information.Comment: 9 pages, 5 figure

Growing networks of overlapping communities with internal structure

We introduce an intuitive model that describes both the emergence of community structure and the evolution of the internal structure of communities in growing social networks. The model comprises two complementary mechanisms: One mechanism accounts for the evolution of the internal link structure of a single community, and the second mechanism coordinates the growth of multiple overlapping communities. The first mechanism is based on the assumption that each node establishes links with its neighbors and introduces new nodes to the community at different rates. We demonstrate that this simple mechanism gives rise to an effective maximal degree within communities. This observation is related to the anthropological theory known as Dunbar's number, i.e., the empirical observation of a maximal number of ties which an average individual can sustain within its social groups. The second mechanism is based on a recently proposed generalization of preferential attachment to community structure, appropriately called structural preferential attachment (SPA). The combination of these two mechanisms into a single model (SPA+) allows us to reproduce a number of the global statistics of real networks: The distribution of community sizes, of node memberships and of degrees. The SPA+ model also predicts (a) three qualitative regimes for the degree distribution within overlapping communities and (b) strong correlations between the number of communities to which a node belongs and its number of connections within each community. We present empirical evidence that support our findings in real complex networks.Comment: 14 pages, 8 figures, 2 table

Multidisciplinary learning through collective performance favors decentralization

Many models of learning in teams assume that team members can share solutions or learn concurrently. However, these assumptions break down in multidisciplinary teams where team members often complete distinct, interrelated pieces of larger tasks. Such contexts make it difficult for individuals to separate the performance effects of their own actions from the actions of interacting neighbors. In this work, we show that individuals can overcome this challenge by learning from network neighbors through mediating artifacts (like collective performance assessments). When neighbors' actions influence collective outcomes, teams with different networks perform relatively similarly to one another. However, varying a team's network can affect performance on tasks that weight individuals' contributions by network properties. Consequently, when individuals innovate (through ``exploring'' searches), dense networks hurt performance slightly by increasing uncertainty. In contrast, dense networks moderately help performance when individuals refine their work (through ``exploiting'' searches) by efficiently finding local optima. We also find that decentralization improves team performance across a battery of 34 tasks. Our results offer design principles for multidisciplinary teams within which other forms of learning prove more difficult.Comment: 11 pages, 8 figures. For SI Appendix, see Ancillary files. For accompanying code, see https://github.com/meluso/multi-disciplinary-learning. For accompanying data, see https://osf.io/kyvtd

Percolation and the effective structure of complex networks

Analytical approaches to model the structure of complex networks can be distinguished into two groups according to whether they consider an intensive (e.g., fixed degree sequence and random otherwise) or an extensive (e.g., adjacency matrix) description of the network structure. While extensive approaches---such as the state-of-the-art Message Passing Approach---typically yield more accurate predictions, intensive approaches provide crucial insights on the role played by any given structural property in the outcome of dynamical processes. Here we introduce an intensive description that yields almost identical predictions to the ones obtained with MPA for bond percolation. Our approach distinguishes nodes according to two simple statistics: their degree and their position in the core-periphery organization of the network. Our near-exact predictions highlight how accurately capturing the long-range correlations in network structures allows to easily and effectively compress real complex network data.Comment: 11 pages, 4 figure