Marián Boguñá Espinal (Barcelona, 1967) is a full professor at the Departament de Física de la Matèria Condensada of the Universitat de Barcelona. He graduated in Physics in 1994 and obtained his PhD also in Physics in 1998. In 1999, he moved to the USA to do a postdoctoral stay with Professor George H. Weiss at the National Institutes of Health, Washington DC. After this period, he moved back to Barcelona where, in 2003, he was awarded a Ramón y Cajal fellowship. He got the tenure position at the end of 2008. During this period, he has also spent several months in the USA as invited guest scientist at Indiana University. M. Boguñá has written over 70 publications in major peer reviewed international scientific journals, book chapters, and conference proceedings. Among those, Nature, Nature Physics, Nature Communications, Proceedings of the National Academy of Sciences US, Physical Review Letters, and Physical Review X. He was the chair of the international conference BCNetWORKSHOP 2008 Trends and Perspectives in Complex Networks and has served as a program committee member in many international conferences. In January 2008, he obtained the Outstanding Referee award of the American Physical Society. In December 2010 and 2015, he was awarded as ICREA Academia researcher2010 and 2015,respectively. Since January 2013 he serves as an editorial board member for Scientific Reports.
PhD in Physics
University of Barcelona
Degree in Physics
University of Barcelona
Dec. 2008 - Present
Associate Professor, Dept. Física Fonamental, Universitat de Barcelona
Jan. 2004 - Nov. 2008
Ramon y Cajal Researcher, Dept. Física Fonamental, Universitat de Barcelona
Aug. 2006 - Dec. 2006
Visiting Guest Scientist, School of Informatics, Indiana University
Sept. 2005 - Dec. 2005
Visiting Guest Scientist, School of Informatics, Indiana University
Dec. 2002 - Dec. 2003
Postdoctoral Researcher, COSIN Project, Fundació Bosch i Gimpera, Universitat de Barcelona
June 2001 - Dec. 2002
Postdoctoral Researcher, Fundació Bosch i Gimpera, Universitat de Barcelona
Apr. 1999 - Dec. 2000
Postdoctoral Fellow, National Institutes of Health, USA. Supervisor: Dr. George H. Weiss
Sept. 1996 - Apr. 1999
Assistant Professor, Dept. Física Fonamental, Universitat de Barcelona
Awards & fellowships
ICREA Academia researcher 2015
ICREA Academia researcher 2010
Outstanding Referee of the American Physical Society
Ramón y Cajal Fellow of the Spanish Ministry of Science
Fogarty Fellow, National Institutes of Health, Washington DC, USA
Hyperbolic geometry of complex networks
Krioukov, D; Papadopoulos, F; Kitsak, M; Vahdat, A; Boguna, M
Physical Review E8236106 (2010)
We develop a geometric framework to study the structure and function of complex networks. We assume that hyperbolic geometry underlies these networks, and we show that with this assumption, heterogeneous degree distributions and strong clustering in complex networks emerge naturally as simple reflections of the negative curvature and metric property of the underlying hyperbolic geometry. Conversely, we show that if a network has some metric structure, and if the network degree distribution is heterogeneous, then the network has an effective hyperbolic geometry underneath. We then establish a mapping between our geometric framework and statistical mechanics of complex networks. This mapping interprets edges in a network as noninteracting fermions whose energies are hyperbolic distances between nodes, while the auxiliary fields coupled to edges are linear functions of these energies or distances. The geometric network ensemble subsumes the standard configuration model and classical random graphs as two limiting cases with degenerate geometric structures. Finally, we show that targeted transport processes without global topology knowledge, made possible by our geometric framework, are maximally efficient, according to all efficiency measures, in networks with strongest heterogeneity and clustering, and that this efficiency is remarkably robust with respect to even catastrophic disturbances and damages to the network structure.
Absence of Epidemic Threshold in Scale-Free Networks with Degree Correlations
Boguna, M; Pastor-Satorras, R; Vespignani, A
PHYSICAL REVIEW LETTERS9028701 (2003)
Random scale-free networks have the peculiar property of being prone to the spreading of infections. Here we provide for the susceptible-infected-susceptible model an exact result showing that a scale-free degree distribution with diverging second moment is a sufficient condition to have null epidemic threshold in unstructured networks with either assortative or disassortative mixing. Degree correlations result therefore irrelevant for the epidemic spreading picture in these scale-free networks. The present result is related to the divergence of the average nearest neighbor´s degree, enforced by the degree detailed balance condition.
Extracting the multiscale backbone of complex weighted networks
Serrano, MA; Boguna, M; Vespignani, A
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA1066483-6488 (2009)
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A large number of complex systems find a natural abstraction in the form of weighted networks whose nodes represent the elements of the system and the weighted edges identify the presence of an interaction and its relative strength. In recent years, the study of an increasing number of large-scale networks has highlighted the statistical heterogeneity of their interaction pattern, with degree and weight distributions that vary over many orders of magnitude. These features, along with the large number of elements and links, make the extraction of the truly relevant connections forming the network´s backbone a very challenging problem. More specifically, coarse-graining approaches and filtering techniques come into conflict with the multiscale nature of large-scale systems. Here, we define a filtering method that offers a practical procedure to extract the relevant connection backbone in complex multiscale networks, preserving the edges that represent statistically significant deviations with respect to a null model for the local assignment of weights to edges. An important aspect of the method is that it does not belittle small-scale interactions and operates at all scales defined by the weight distribution. We apply our method to real-world network instances and compare the obtained results with alternative backbone extraction techniques.
Fragkiskos Papadopoulos, Maksim Kitsak, M. Ćngeles Serrano, MariĆ”n BoguĆ±Ć”, and Dmitri Krioukov
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The principle that ‘popularity is attractive’ underlies preferential attachment, which is a common explanation for the emergence of scaling in growing networks. If new connections are made preferentially to more popular nodes, then the resulting distribution of the number of connections possessed by nodes follows power laws, as observed in many real networks. Preferential attachment has been directly validated for some real networks (including the Internet), and can be a consequence of different underlying processes based on node fitness, ranking, optimization, random walks or duplication. Here we show that popularity is just one dimension of attractiveness; another dimension is similarity. We develop a framework in which new connections optimize certain trade-offs between popularity and similarity, instead of simply preferring popular nodes.The framework has a geometric interpretation in which popularity preference emerges from local optimization. As opposed to preferential attachment, our optimization framework accurately describes the large-scale evolution of technological (the Internet), social (trust relationships between people) and biological (Escherichia coli metabolic) networks, predicting the probability of new links with high precision.The framework that we have developed can thus be used for predicting new links in evolving networks, and provides a different perspective on preferential attachment as an emergent phenomenon.
Class of correlated random networks with hidden variables
Boguna, M; Pastor-Satorras, R
PHYSICAL REVIEW E6836112 (2003)
We study a class of models of correlated random networks in which vertices are characterized by hidden variables controlling the establishment of edges between pairs of vertices. We find analytical expressions for the main topological properties of these models as a function of the distribution of hidden variables and the probability of connecting vertices. The expressions obtained are checked by means of numerical simulations in a particular example. The general model is extended to describe a practical algorithm to generate random networks with an a priori specified correlation structure. We also present an extension of the class, to map nonequilibrium growing networks to networks with hidden variables that represent the time at which each vertex was introduced in the system.
Navigability of complex networks
Boguna, M; Krioukov, D; Claffy, KC
Nature Physics574-80 (2009)
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Routing information through networks is a universal phenomenon in both natural and man-made complex systems. When each node has full knowledge of the global network connectivity, finding short communication paths is merely a matter of distributed computation. However, in many real networks, nodes communicate efficiently even without such global intelligence. Here, we show that the peculiar structural characteristics of many complex networks support efficient communication without global knowledge. We also describe a general mechanism that explains this connection between network structure and function. This mechanism relies on the presence of a metric space hidden behind an observable network. Our findings suggest that real networks in nature have underlying metric spaces that remain undiscovered. Their discovery should have practical applications in a wide range of areas where networks are used to model complex systems.
The Internet infrastructure is severely stressed. Rapidly growing overheads associated with the primary function of the Internet—routing information packets between any two computers in the world—cause concerns among Internet experts that the existing Internet routing architecture may not sustain even another decade. In this paper, we present a method to map the Internet to a hyperbolic space. Guided by a constructed map, which we release with this paper, Internet routing exhibits scaling properties that are theoretically close to the best possible, thus resolving serious scaling limitations that the Internet faces today. Besides this immediate practical viability, our network mapping method can provide a different perspective on the community structure in complex networks.
Anna Saumell-Mendiola, M. Ćngeles Serrano, and MariĆ”n BoguĆ±Ć”
Physical Review E86026106 (2012)
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Many real networks are not isolated from each other but form networks of networks, often interrelated in non trivial ways. Here, we analyze an epidemic spreading process taking place on top of two interconnected complex networks. We develop a heterogeneous mean field approach that allows us to calculate the conditions for the emergence of an endemic state. Interestingly, a global endemic state may arise in the coupled system even though the epidemics is not able to propagate on each network separately, and even when the number of coupling connections is small. Our analytic results are successfully confronted against large-scale numerical simulations.
Self-similarity of complex networks and hidden metric spaces
Serrano, MA; Krioukov, D; Boguna, M
PHYSICAL REVIEW LETTERS10078701 (2008)
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We demonstrate that the self-similarity of some scale-free networks with respect to a simple degree-thresholding renormalization scheme finds a natural interpretation in the assumption that network nodes exist in hidden metric spaces. Clustering, i.e., cycles of length three, plays a crucial role in this framework as a topological reflection of the triangle inequality in the hidden geometry. We prove that a class of hidden variable models with underlying metric spaces are able to accurately reproduce the self-similarity properties that we measured in the real networks. Our findings indicate that hidden geometries underlying these real networks are a plausible explanation for their observed topologies and, in particular, for their self-similarity with respect to the degree-based renormalization.
Nature of the Epidemic Threshold for the Susceptible-Infected-Susceptible Dynamics in Networks
MariĆ”n BoguĆ±Ć”, Claudio Castellano, and Romualdo Pastor-Satorras
Physical Review Letters111068701 (2013)
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We develop an analytical approach to the susceptible-infected-susceptible epidemic model that allows us to unravel the true origin of the absence of an epidemic threshold in heterogeneous networks. We find that a delicate balance between the number of high degree nodes in the network and the topological distance between them dictates the existence or absence of such a threshold. In particular, small-world random networks with a degree distribution decaying slower than an exponential have a vanishing epidemic threshold in the thermodynamic limit.
Percolation and Epidemic Thresholds in Clustered Networks
Serrano, MA; Boguna, M
PHYSICAL REVIEW LETTERS9788701 (2006)
We develop a theoretical approach to percolation in random clustered networks. We find that, although clustering in scale-free networks can strongly affect some percolation properties, such as the size and the resilience of the giant connected component, it cannot restore a finite percolation threshold. In turn, this implies the absence of an epidemic threshold in this class of networks, thus extending this result to a wide variety of real scale-free networks which shows a high level of transitivity. Our findings are in good agreement with numerical simulations.
Dmitri Krioukov, Maksim Kitsak, Robert S. Sinkovits, David Rideout, David Meyer, MariĆ”n BoguĆ±Ć”
Scientific Reports2793 (2012)
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Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.
Random scale-free networks are ultrasmall worlds. The average length of the shortest paths in networks of size N scales as lnlnN. Here we show that these ultrasmall worlds can be navigated in ultrashort time. Greedy routing on scale-free networks embedded in metric spaces finds paths with the average length scaling also as lnlnN. Greedy routing uses only local information to navigate a network. Nevertheless, it finds asymptotically the shortest paths, a direct computation of which requires global topology knowledge. Our findings imply that the peculiar structure of complex networks ensures that the lack of global topological awareness has asymptotically no impact on the length of communication paths. These results have important consequences for communication systems such as the Internet, where maintaining knowledge of current topology is a major scalability bottleneck.
Hidden geometric correlations in real multiplex networks
Kaj-Kolja Kleineberg, MariĆ”n BoguĆ±Ć”, M. Ćngeles Serrano, and Fragkiskos Papadopoulos
Nature Physics121076 1081(2016)
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Real networks often form interacting parts of larger and more complex systems. Examples can be found in di erent domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in di erent real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow e cient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are su ciently strong.
Double percolation phase transition in clustered complex networks
Pol Colomer-de-SimĆ³n and MariĆ”n BoguĆ±Ć”
Physical Review X4041020 (2014)
The internal organization of complex networks often has striking consequences on either their response to external perturbations or on their dynamical properties. In addition to small-world and scale-free properties, clustering is the most common topological characteristic observed in many real networked systems. In this paper, we report an extensive numerical study on the effects of clustering on the structural properties of complex networks. Strong clustering in heterogeneous networks induces the emergence of a core-periphery organization that has a critical effect on the percolation properties of the networks. We observe a novel double phase transition with an intermediate phase in which only the core of the network is percolated and a final phase in which the periphery percolates regardless of the core. This result implies breaking of the same symmetry at two different values of the control parameter, in stark contrast to the modern theory of continuous phase transitions. Inspired by this core-periphery organization, we introduce a simple model that allows us to analytically prove that such an anomalous phase transition is in fact possible.
The geometric nature of weights in real complex networks
The topology of many real complex networks has been conjectured to be embedded in hidden metric spaces, where distances between nodes encode their likelihood of being connected. Besides of providing a natural geometrical interpretation of their complex topologies, this hypothesis yields the recipe for sustainable Internet’s routing protocols, sheds light on the hierarchical organization of biochemical pathways in cells, and allows for a rich character- ization of the evolution of international trade. Here we present empirical evidence that this geometric interpretation also applies to the weighted organization of real complex networks. We introduce a very general and versatile model and use it to quantify the level of coupling between their topology, their weights and an underlying metric space. Our model accurately reproduces both their topology and their weights, and our results suggest that the formation of connections and the assignment of their magnitude are ruled by different processes.
M. Ćngeles Serrano, Dmitri Krioukov, and MariĆ”n Boguna
Physical Review Letters106048701 (2011)
We provide a simple proof that graphs in a general class of self-similar networks have zero percolation threshold. The considered self-similar networks include random scale-free graphs with given expected node degrees and zero clustering, scale-free graphs with finite clustering and metric structure, growing scale-free networks, and many real networks. The proof and the derivation of the giant component size do not require the assumption that networks are treelike. Our results rely only on the observation that self-similar networks possess a hierarchy of nested subgraphs whose average degree grows with their depth in the hierarchy. We conjecture that this property is pivotal for percolation in networks.
Geometric correlations mitigate the extreme vulnerability of multiplex networks against targeted attacks
Kaj-Kolja Kleineberg, Lubos Buzna, Fragkiskos Papadopoulos, MariĆ”n BoguĆ±Ć”, and M. Ćngeles Serrano
Physical Review Letters118218301 (2017)
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We show that real multiplex networks are unexpectedly robust against targeted attacks on high-degree nodes and that hidden interlayer geometric correlations predict this robustness. Without geometric correlations, multiplexes exhibit an abrupt breakdown of mutual connectivity, even with interlayer degree correlations. With geometric correlations, we instead observe a multistep cascading process leading into a continuous transition, which apparently becomes fully continuous in the thermodynamic limit. Our results are important for the design of efficient protection strategies and of robust interacting networks in many domains.
We introduce Mercator, a reliable embedding method to map real complex networks into their hyperbolic latent geometry. The method assumes that the structure of networks is well described by the Popularity×Similarity S1/H2 static geometric network model, which can accommodate ar- bitrary degree distributions and reproduces many pivotal properties of real networks, including self-similarity patterns. The algorithm mixes machine learning and maximum likelihood approaches to infer the coordinates of the nodes in the underlying hyperbolic disk with the best matching be- tween the observed network topology and the geometric model. In its fast mode, Mercator uses a model-adjusted machine learning technique performing dimensional reduction to produce a fast and accurate map, whose quality already outperform other embedding algorithms in the literature. In the refined Mercator mode, the fast-mode embedding result is taken as an initial condition in a Max- imum Likelihood estimation, which significantly improves the quality of the final embedding. Apart from its accuracy as an embedding tool, Mercator has the clear advantage of systematically inferring not only node orderings, or angular positions, but also the hidden degrees and global model parame- ters, and has the ability to embed networks with arbitrary degree distributions. Overall, our results suggest that mixing machine learning and maximum likelihood techniques in a model-dependent framework can boost the meaningful mapping of complex networks.
MariĆ”n BoguĆ±Ć”, Ivan Bonamassa, Manlio De Domenico, Shlomo Havlin, Dmitri Krioukov, and M. Ćngeles Serrano
Nature Reviews Physics3114 135(2021)
Networks are finite metric spaces, with distances defined by the shortest paths between nodes. However, this is not the only form of network geometry: two others are the geometry of latent spaces underlying many networks and the effective geometry induced by dynamical processes in networks. These three approaches to network geometry are intimately related, and all three of them have been found to be exceptionally efficient in discovering fractality, scale invariance, self-similarity and other forms of fundamental symmetries in networks. Network geometry is also of great use in a variety of practical applications, from understanding how the brain works to routing in the Internet. We review the most important theoretical and practical developments dealing with these approaches to network geometry and offer perspectives on future research directions and challenges in this frontier in the study of complexity.
MariĆ”n BoguĆ±Ć”, Dmitri Krioukov, Pedro Almagro, and M. Ćngeles Serrano
Physical Review Research2023040 (2020)
Networks with underlying metric spaces attract increasing research attention in network science,statistical physics, applied mathematics, computer science, sociology, and other fields. This atten-tion is further amplified by the current surge of activity in graph embedding. In the vast realm ofspatial network models, only a few reproduce even the most basic properties of real-world networks.Here, we focus on three such properties—sparsity, small worldness, and clustering—and identify thegeneral subclass of spatial homogeneous and heterogeneous network models that are sparse smallworlds and that have nonzero clustering in the thermodynamic limit. We rely on the maximum entropy approach where network links correspond to noninteracting fermions whose energy dependenceon spatial distances determines network small worldness and clustering.
Memory-induced complex contagion in epidemic spreading
Xavier R. Hoffmann and MariĆ”n BoguĆ±Ć”
New Journal of Physics21033034 (2019)
Albeit epidemic models have evolved into powerful predictive tools for the spread of diseases andopinions, most assume memoryless agents and independent transmission channels. We develop an in-fection mechanism that is endowed with memory of past exposures and simultaneously incorporatesthe joint effect of multiple infectious sources. Analytic equations and simulations of the susceptible-infected-susceptible model in unstructured substrates reveal the emergence of an additional phasethat separates the usual healthy and endemic ones. This intermediate phase shows fundamentallydistinct characteristics, and the system exhibits either excitability or an exotic variant of bistability. Moreover, the transition to endemicity presents hybrid aspects. These features are the product of anintricate balance between two memory modes and indicate that non-Markovian effects significantlyalter the properties of spreading processes
Quantifying Human Engagement into Playful Activities
David Reguera, Pol Colomer-de-SimĆ³n, IvĆ”n Encinas, Manel Sort, Jan Wedekind, and MariĆ”n BoguĆ±Ć”
Sci. Rep104145 (2020)
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Engaging in playful activities, such as playing a musical instrument, learning a language, or per- forming 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 even- tually 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.
Real networks often grow through the sequential addition of new nodes that connect to older ones in the graph. However, many real systems evolve through the branching of fundamental units, whether those be scientific fields, countries, or species. Here, we provide empirical evidence for self-similar growth of network structure in the evolution of real systems—the journal-citation network and the world trade web—and present the geomet- ric branching growth model, which predicts this evolution and explains the symmetries observed. The model produces multiscale unfolding of a network in a sequence of scaled-up replicas preserving network features, including clustering and community structure, at all scales. Practical applications in real instances include the tuning of network size for best response to external influence and finite-size scaling to assess critical behavior under random link failures.
An anomalous topological phase transition in spatial random graphs
Jasper van der Kolk, M. Ćngeles Serrano, and MariĆ”n BoguĆ±Ć”
Communications Physics5245 (2022)
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Clustering–the tendency for neighbors of nodes to be connected–quantifies the coupling of a complex network to its latent metric space. In random geometric graphs, clustering undergoes a continuous phase transition, separating a phase with finite clustering from a regime where clustering vanishes in the thermodynamic limit. We prove this geometric to non-geometric phase transition to be topological in nature, with anomalous features such as diverging entropy as well as atypical finite-size scaling behavior of clustering. Moreover, a slow decay of clustering in the non-geometric phase implies that some real networks with relatively high levels of clustering may be better described in this regime.
Detecting the ultra low dimensionality of real networks
Pedro Almagro, MariĆ”n BoguĆ±Ć”, and M. Ćngeles Serrano
Nature Communications136096 (2022)
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Reducing dimension redundancy to find simplifying patterns in high-dimensional datasets and complex networks has become a major endeavor in many scientific fields. However, detecting the dimensionality of their latent space is challenging but necessary to generate efficient embeddings to be used in a multitude of downstream tasks. Here, we propose a method to infer the dimensionality of networks without the need for any a priori spatial embedding. Due to the ability of hyperbolic geometry to capture the complex connectivity of real networks, we detect ultra low dimensionality far below values reported using other approaches. We applied our method to real networksfrom different domains and found unexpected regularities, including: tissue-specific biomolecular networks being extremely low dimensional; brain connectomes being close to the three dimensions of their anatomical embedding; and social networks and the Internet requiring slightly higher dimensionality. Beyond paving the way towards an ultra efficient dimensional reduction, our findings help address fundamental issues that hinge on dimensionality, such as universality in critical behavior.
Elements in Structure and Dynamics of Complex Networks. Cambridge University Press(2021)
Real networks comprise from hundreds to millions of interacting elements and permeate all contexts, from technology to biology to society. All of them display non-trivial connectivity patterns, including the small-world phenomenon, making nodes to be separated by a small number of intermediate links. As a consequence, networks present an apparent lack of metric structure and are difficult to map. Yet, many networks have a hidden geometry that enables meaningful maps in the two-dimensional hyperbolic plane. The discovery of such hidden geometry and the understanding of its role have become fundamental questions in network science giving rise to the field of network geometry. This Element reviews fundamental models and methods for the geometric description of real networks with a focus on applications of real network maps, including decentralized routing protocols, geometric community detection, and the self-similar multiscale unfolding of networks by geometric renormalization.
Turing patterns, arising from the interplay between competing species of diffusive particles, have long been an important concept for describing nonequilibrium self-organization in nature and have been extensively investigated in many chemical and biological systems. Historically, these patterns have been studied in extended systems and lattices. Recently, the Turing instability was found to produce topological patterns in networks with scale-free degree distributions and the small-world property, although with an apparent absence of geometric organization. While hints of explicitly geometric patterns in simple network models (e.g., Watts-Strogatz) have been found, the question of the exact nature and morphology of geometric Turing patterns in heterogeneous complex networks remained unresolved. In this work, we study the Turing instability in the framework of geometric random graph models, where the network topology is explained using an underlying geometric space. We demonstrate that not only can geometric patterns be observed, their wavelength can also be estimated by studying the eigenvectors of the annealed graph Laplacian. Finally, we show that Turing patterns can be found in geometric embeddings of real networks. These results indicate that there is a profound connection between the function of a network and its hidden geometry, even when the associated dynamical processes are exclusively determined by the network topology.
First-principle network models are crucial to understanding the intricate topology of real complex networks. Although modelling efforts have been quite successful in undirected networks, generative models for networks with asymmetric interactions are still not well developed and unable to reproduce several basic topological properties. Progress in this direction is of particular interest, as real directed networks are the norm rather than the exception in many natural and human-made complex systems. Here we show how the network geometry paradigm can be extended to the case of directed networks. We define a maximum entropy ensemble of random geometric directed graphs with a given sequence of in-degrees and out-degrees. Beyond these local properties, the ensemble requires only two additional parameters to fix the levels of reciprocity and the frequency of the seven possible types of three-node cycles in directed networks. A systematic comparison with several representative empirical datasets shows that fixing the level of reciprocity alongside the coupling with an underlying geometry is able to reproduce the wide diversity of clustering patterns observed in real directed complex networks.
D-Mercator: multidimensional hyperbolic embedding of real networks
Robert Jankowski, Antoine Allard, MariĆ”n BoguĆ±Ć”, and M. Ćngeles Serrano
Nature Communications147585 (2023)
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One of the pillars of the geometric approach to networks has been the development of model-based mapping tools that embed real networks in its latent geometry. In particular, the tool Mercator embeds networks into the hyperbolic plane. However, some real networks are better described by the multidimensional formulation of the underlying geometric model. Here, we introduce D-Mercator, a model-based embedding method that produces multidimensional maps of real networks into the (D + 1)-hyperbolic space, where the similarity subspace is represented as a D-sphere. We used D-Mercator to produce multidimensional hyperbolic maps of real networks and estimated their intrinsic dimensionality in terms of navigability and community structure. Multidimensional representations of real networks are instrumental in the identification of factors that determine connectivity and in elucidating fundamental issues that hinge on dimensionality, such as the presence of universality in critical behavior.
Feature-enriched network geometry explains graph-structured data
Roya Aliakbarisani, M. Ćngeles Serrano, and MariĆ”n BoguĆ±Ć”
Graph-structured data provide a comprehensive description of complex systems, encompassing not only the interactions among nodes but also the intrinsic features that characterize these nodes. These features play a fundamental role in the formation of links within the network, making them valuable for extracting meaningful topological information. Notably, features are at the core of deep learning techniques such as Graph Convolutional Neural Networks (GCNs) and offer great utility in tasks like node classification, link prediction, and graph clustering. In this letter, we present a comprehensive framework that treats features as tangible entities and establishes a bipartite graph connecting nodes and features. By assuming that nodes sharing similarities should also share features, we introduce a geometric similarity space where both nodes and features coexist, shaping the structure of both the node network and the bipartite network of nodes and features. Through this framework, we can identify correlations between nodes and features in real data and generate synthetic datasets that mimic the topological properties of their connectivity patterns. The approach provides insights into the inner workings of GCNs by revealing the intricate structure of the data.