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 characterization of the evolution of international trade. We present empirical evidence that this geometric interpretation also applies to the weighted organisation 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.
Most of the complexity of networks is encoded into the intricate topology of the interactions among their components and into the layout of the intensities associated to such interactions (i.e., the weights). Interestingly, weights are coupled in a non-trivial way to the binary network topology, playing a central role in their structural organisation, function, and dynamics Barrat et al. (2004a). For instance, the quantification of the rich-club effect in real weighted networks, in sharp contrast to results in unweighted representations, unveils the formation of alliances in multipolarised environments or the lack of cohesion even in the presence of rich-club ordering Serrano (2008). Similarly, the propagation of emergent diseases in the international airports network is intimately linked to the number of passengers flying from one airport to the other Brockmann and Helbing (2013). A shift towards a paradigm of weighted networks is therefore in order to fully understand the behaviour and evolution of complex networks. However, advances in this area have been limited by the extreme heterogeneity and fluctuations that typically characterise the distribution of weights.
Meanwhile, complex networks Newman (2010); Cohen and Havlin (2010) have been conjectured to be embedded in hidden metric spaces, in which distances among nodes encode a balance between their similarity and popularity and, thus, determine their likelihood of being connected Serrano et al. (2008). This hypothesis, combined with a suitable underlying space, has offered a geometric interpretation of the complex topologies observed in real networks, including scale-free degree distributions, the small-world effect, strong clustering, community structure, and self-similarity. A metric space underlying complex networks can also explain their efficient inter-node communication without knowledge of the complete structure Boguñá and Krioukov (2009); Boguñá et al. (2009). Moreover, it has been shown that for networks whose degree distribution is scale-free, the natural geometry of their underlying metric space is hyperbolic Krioukov et al. (2010); Gugelmann et al. (2012); Bode et al. (2013); Candellero and Fountoulakis (2014); Friedrich and Krohmer (2015); Aste et al. (2005). All these results have then been used to propose geometric models for real growing networks that reproduce their evolution and in which preferential attachment emerges from local optimisation principles Papadopoulos et al. (2012); Gulyás et al. (2015). Finally, mapping real complex networks into a hidden metric space has yielded a sustainable solution to the scaling limitations of the Internet Boguñá et al. (2010), has shed light on the hierarchical organisation of biochemical pathways in cells Serrano et al. (2012), and has allowed a rich characterization of the evolution of international trade over fourteen decades García-Pérez et al. (2016).
In real weighted networks, weights are coupled to the binary topology in a non-trivial way. This is manifested, for instance, in a non-linear relation between the strength of a node (the sum of the total weight attached to it) and its degree of the form Barrat et al. (2004a); Popović et al. (2012); Barthélemy (2011). However, the relation between the layout of weights and the geometry underlying the network is unclear. The reason being that, even if the existence of a link depends on the metric distance between the nodes, there is no reason, a priori, to expect that the same distance will affect its weight. For instance, in the airports network, the decision to set up a link between two cities depends on the airline companies operating at the two airports, a process affected by geopolitic and economic costs and by the expected flow of passengers that would eventually compensate such costs. However, once the connection is established, its weight is determined by the aggregation of the individual decisions of people using it, a process that may be affected by a different cost function.
In this paper, we present empirical evidence on the metric nature of weights in real biological, economic and transportation networks (see Methods for a description of the datasets), which suggests that the hidden/latent geometry paradigm can be extended to weighted complex networks. We then propose a general class of weighted networks embedded in hidden metric spaces that accurately reproduces many properties observed in real weighted networks. This model has the critical ability to fix the degree-strength distribution independently of the coupling of the topology and weighted organisation with the metric space. It is therefore possible to isolate, and thus directly study, the effect of the coupling between the metric space and the weighted organisation of real weighted networks. In fact, our results unveil that in some systems these couplings are uncorrelated, which in turn suggests that the formation of connections and the assignment of their magnitude might be ruled by different processes. Our empirical findings, combined with our new class of models, open the path towards the use of information encoded in the weights of the links to find more accurate embeddings of real networks, which in turn will improve the detection of communities, the prediction of missing links and provide estimates for the weights of such missing links.
Interplay between weights and triangles in real networks
Clustering, as a reflection of the triangle inequality, is the key topological property coupling the bare topology of a complex system and its effective underlying metric space Serrano et al. (2008). In this context, the triangle inequality stipulates that if nodes A and B are close, and nodes A and C are also close, we expect nodes B and C to be close as well; triangles are therefore more likely to exist between nodes that are nearby. Consequently, we expect that if the weights of connections depend on the distance between the connected nodes in the underlying metric space, they should be quantitatively different depending on the clustering properties of the connections. However, weights and clustering are known to be strongly influenced by the degrees of endpoint nodes Barrat et al. (2004a); Popović et al. (2012); Pajevic and Plenz (2012), which prevents from a direct detection of the metric properties of weights due to the typical heterogeneity in the degrees of nodes in real networks. Thus, to compare links on an equal footing, we define the normalised weight of an existing link connecting nodes and as , where is the average weight of links as a function of the product of degrees of their endpoint nodes. By doing so, we decouple the weights and the topology, leaving the normalised weights seemingly randomly fluctuating around 1 (see uniform sampling on Fig. 1).
Figure 1 shows, however, that these fluctuations are not uniform as links involved in triangles tend to have larger normalised weights than the average link. Indeed, in some cases the difference can reach more than %. Sampling links over triangles is equivalent to sampling links proportionally to their multiplicity (i.e., the number of triangles to which a link participates). Therefore, the results in Fig. 1 indicate that and are positively correlated variables, as corroborated by their Pearson correlation coefficient (see Supplementary Table 1). In Ref. Pajevic and Plenz (2012), the authors also found local correlations between the multiplicity of links and the weights for different real networks. Note however that in that study weights were not normalised to discount the effects of the heterogeneity in the degrees of the nodes, so that the detected weighted organisation can not be taken as a signature of underlying metric properties.
Since triangles are a reflection of the triangle inequality in the underlying metric space, we expect nodes forming triangles to be close to one another. Thus, the higher average normalised weight observed on triangles strongly suggests a metric nature of weights, which is not a trivial consequence of the relation between weights and topology. This leads us to formulate the hypothesis that the same underlying metric space ruling the network topology—inducing the existence of strong clustering as a reflection of the triangle inequality in the underlying geometry—is also inducing the observed correlation between and . To prove this, we develop a realistic model of geometric weighted random networks, which allows us to estimate the coupling between weights and geometry in real networks.
A geometric model of weighted networks
|World Trade||Economic||Countries||US dollars||2.42||1.6||5.8||189|
|Cargo ships||Transportation||Ports||Shipping journeys||2.03||1.05||10.4||834|
|US Commodities||Economic||Economic sectors||US dollars||2.46||1.22||5.8||376|
|E. Coli||Biological||Metabolites||Common reactions||2.52||1.1||6.6||1100|
|Human brain||Biological||Brain regions||Connection density||7.14||0.86||24.1||501|
Many models have been proposed to generate weighted networks. Among them, growing network models Bianconi (2004); Barrat et al. (2004b); Yook et al. (2001); Kumpula et al. (2007); Antal and Krapivsy (2005); Zheng et al. (2003); Wang et al. (2005); Li et al. (2006) and the maximum-entropy class of models Mastrandrea et al. (2014); Garlaschelli (2009); Garlaschelli and Loffredo (2009); Sagarra et al. (2013, 2015). However, none of them is general enough to reproduce simultaneously the topology and weighted structure of real weighted complex networks. We introduce a new model based on a class of random networks with hidden variables embedded in a metric space Serrano et al. (2008); Boguñá and Krioukov (2009) that overcomes these limitations. In this model, nodes are uniformly distributed with constant density in a -dimensional homogeneous and isotropic metric space (see Supplementary Methods), and are assigned a hidden variable according to the probability density function (pdf) . Two nodes with hidden variables and separated by a metric distance are connected with a probability
where is a free parameter fixing the average degree and is an arbitrary positive function taking values within the interval . The free parameter can be chosen such that . Hence, corresponds to the expected degree of nodes, so the degree distribution can be specified through the pdf regardless of the specific form of (see Supplementary Methods). The freedom in the choice of allows us to tune the level of coupling between the topology of the networks and the metric space, which in turn allows us to control many properties such as the clustering coefficient and the navigability Serrano et al. (2008); Boguñá et al. (2009).
To generate weighted networks, a second hidden variable is associated to each node. This new hidden variable can be correlated with so, hereafter, we assume that the pair of hidden variables associated with the same node are drawn from the joint pdf . The weight of an existing link between two nodes with hidden variables , , and , respectively, and at a metric distance is given by
with and and where is a positive random variable drawn from the probability density function . Notice that dictates a trade-off between the contribution of degrees and geometry to weights. If weights are independent of the underlying metric space and maximally dependent on degrees, while implies that weights are maximally coupled to the underlying metric space with no direct contribution of the degrees. Equation (2) constitutes the keystone of our model. Indeed, as shown in the Supplementary Methods, the form of Eq. (2) is the only one ensuring that . The free parameter can then always be chosen such that . The new hidden variable can therefore be interpreted as the expected strength of a node, and the joint pdf controls the correlation between degrees and strengths in the network. Indeed, as shown in the Supplementary Methods, the average strength of nodes with a given degree, , relates to the first moment of the conditional pdf , , so that when then .
The relations and —and consequently the relation between and the degree-strength distribution—hold independently of the specific form of the connection probability and of the noise distribution . Besides conferring great versatility to our model, this conveys a degree of control over the weight distribution which is independent of the specification of degrees and strengths and, more importantly, opens the possibility to measure the metric properties of complex weighted networks.
To use the model in the context of real weighted networks, we choose the circle of radius to be the underlying geometry, i.e. , over which nodes are uniformly distributed Serrano et al. (2008). Distances among nodes are measured in terms of arc lengths, that is, two nodes with angular positions and are at a distance where . The connection probability is set to
where is a free parameter that can be used to tune the clustering and quantifies the level of coupling between the network topology and the metric space. Equation (3) casts the ensemble of networks generated by the model into exponential random networks Krioukov et al. (2010), i.e., networks that are maximally random given the constraints imposed by the free parameters (i.e., and ). To obtain a scale-free degree distribution, hidden variables are distributed according to with and .
Weights are assigned on top of the topology generated by the model. The noise distribution is chosen to be a gamma distribution of average with a given second moment . Finally, to control the correlation between strength and degree and, therefore, to tune the strength distribution, we assume a deterministic relation between hidden variables and of the form , as observed in real complex networks Barrat et al. (2004a); Popović et al. (2012); Barthélemy (2011), yielding (see Supplementary Methods). Notice that the relation between average strength and degree in the previous expression is totally independent of the underlying metric space, which implies that the strength distribution scales as for with . All these theoretical predictions and the ones derived in the Supplementary Methods are confirmed in Supplementary Figure 1.
Hidden metric spaces underlying real weighted networks
At the beginning of this section, we showed that the normalised weights of links participating in triangles are higher, thus suggesting a coupling between the weighted organisation of real weighted complex networks and an underlying metric space. We then presented a model that has the critical ability to fix the joint degree-strength distribution while, independently, varying the level of coupling between the weights and the metric space (parameter ). This opens the way to a definite proof of the geometric nature of weights in real complex networks, which inevitably must involve the triangle inequality: the most fundamental property of any metric space.
For unweighted networks, a direct verification of the triangle inequality based on the topology without an embedding in a metric space is not possible, due to the probabilistic nature of the relationship between the binary structure and the distance between nodes. In contrast, weights do contain information about their distances in the metric space [via Eq. (2)] such that a direct verification of the triangle inequality is possible. To ensure that the metric properties of triples in the network are in correspondence to the metric properties of the corresponding triangles in the underlying space, only triples of nodes forming triangles in the network are taken into account to evaluate the triangle inequality. There are however two main challenges when one tries to apply this methodology. The first one is related to the fact that connections in the weighted model depend not only on angular distances but also on hidden degrees such that we need a purely geometrical formulation of the weighted hidden metric space network model in which angular distances and degrees are combined into a single distance measure. The second issue is related to the intrinsic noise present in the system due to the stochastic nature of the processes conforming it, which may blur the evaluation of the triangle inequality. Below, we propose a way to overcome these two issues.
First, as shown in Krioukov et al. (2010), the model described by Eq. (1) is equivalent, in the one dimensional case, to a purely geometric model where nodes are embedded within a disk of radius in the hyperbolic plane of constant curvature . Indeed, by mapping the hidden variable to a radial coordinate as follows
and keeping the same angular coordinates, the connection probability Eq. (1) can be written as
where is a very good approximation of the hyperbolic distance between two points with radial coordinates and and angular separation . In this framework, networks generated with our model are geometric random networks in the hyperbolic plane, a geometry in which the triangle inequality must hold. To test the triangle inequality, we therefore select nodes participating in topological triangles in the network and measure the hyperbolic distance between them.
The purely geometric interpretation of our model given by Eq. (5) further illustrates the reasons for which a metric space implies a non-vanishing clustering even in the thermodynamical limit. As stated at the beginning of this section, the triangle inequality—a fundamental property of any metric space, including the hyperbolic plane—stipulates that whenever point A is close to point B and point B is close to point C, then points A and C are also close. Consequently, the notion of “closeness” extends well beyond pairwise comparisons and is integrated “at once” in the positions in the metric space. This implies that many-body interactions emerge from pairwise interactions, such as the connection probability given by Eq. (3). Given that nearby nodes are likely to be connected, clustering is a direct consequence of such many-body interactions; any triad of close nodes are likely to form a triangle, independently of the size of the disk, and therefore of the total number of nodes.
from which we can isolate the hyperbolic distance, , between nodes and . The triangle inequality, , then becomes
The first term in the left hand side of this inequality is a function of the actual weights and network topology and, thus, can be empirically estimated in any network. The next two terms on the left hand side have an explicit dependence on the parameter (note that also depends on ). The term in the right hand side is a noise term whose mean value is close to zero.
Let us first assume that this noise term is zero. In synthetic weighted networks, the inequality should hold approximately for any value of in Eq. (7) equal to or larger than the value of used to assign weights in the network. Note that it may not hold exactly even when is greater than its real value due to the inherent noise in the estimation of the hidden variables and in Eq. (7), as well as the global parameters and (note that whenever we set , parameter becomes a function of , see Supplementary Methods). To minimise such uncertainty, we choose and approximate by the degree of nodes. We propose to consider in Eq. (7) as a free parameter and to measure the triangle inequality violation spectrum, , defined as the fraction of violations of the triangle inequality (i.e., triangles for which the left hand side of Eq. (7) is positive). In the absence of noise, should take a very small value when if the weighted structure of the network is congruent with the existence of an underlying metric space. In Fig. 2a, we show for synthetic networks generated with the model with different values of . As expected, the curves fall rapidly precisely at , indicated by the dashed vertical lines.
In real situations, however, noise is typically present and has an impact on . Indeed, Fig. 2b shows its behaviour for a fixed value of and different values of the noise . This implies that we need an independent measure of the noise to infer the value of from the spectrum . For this purpose, we use the square of the coefficient of variation of the strength, which depends linearly on the noise (see the Supplementary Methods). Combining these observations, we propose a procedure to infer the value of for any real complex network based on the empirical . The method is described in details in the Supplementary Methods.
Figures 3a and b show the curves for the real networks and the same curves for synthetic networks generated by our model using the inferred to be maximally congruent with the real data. In all cases, we find a very good agreement between theory and observations, which suggests a coupling with a hidden metric space as a highly plausible explanation of the observed weighted organisation. Note that the increase of for is an expected artifact of Eq. (7) (see Supplementary Methods). Figure 3c shows the values of (coupling topology and metric space) and (coupling weights and metric space) inferred by our method. Notice that, except for the US airports network, is always above 0.40, which indicates a clear and strong coupling between weights and the hidden underlying geometry. We also generated synthetic networks with the inferred parameters and confronted their topological and weighted properties against those of their real counterparts (see Fig. 4 and Supplementary Methods for other networks and a comparison with other models). In all cases, the agreement between the model and the real networks is excellent. Remarkably, in the case of the weight distribution and disparity measure, such agreement is only achieved with the empirical value of found via the test of the triangle inequality.
Finally, we considered the networks for which an embedding of the binary structure was available and rescaled each weight by a factor [see Eq. (6)] where is the hyperbolic distance between nodes in the embeddings Boguñá et al. (2010). We then normalised and sampled the weights as in Fig. 1 and the results are shown in Fig. 3d. Strikingly, we see that the gap observed in Fig. 1 completely disappears in some of the networks or is significantly reduced in others. While the remaining gaps may be due to imprecisions in the embedding (i.e. the embedding procedure cannot take into account the information contained in the weights yet), these results nevertheless add their voice to the evidence pointing toward the geometric nature of the weights in real complex networks.
The metric character of many real complex networks—in which clustering is a direct consequence of the triangle inequality—has long been established. However, the metric nature of their weighted organisation still remained an open question. In this paper, we provided strong empirical evidence for the metric origin of the weighted architecture of real complex networks from very different domains. Our results suggest that the same underlying metric space ruling the network topology also shapes its weighted organisation. It is important to notice that the distances between nodes implied by this metric space does not necessarily correspond to geographic distances (e.g., distances between ports on the Earth) but are rather abstract and effective distances encoding several factors affecting the existence of connections and their intensity.
To account for these empirical findings, we proposed a very general model capable of reproducing the coupling with the metric space in a very simple and elegant way. This model allows us to fix the local properties of the nodes—their joint degree-strength distribution—while varying the coupling of the topology and, independently, of the weights with the hidden metric space. This critical property permits us to gauge quantitatively the effect of the metric space in real systems. In the case of the US airports network, we found quite remarkably that while the coupling between the topology and the metric space is relatively strong, the coupling at the weighted level is quite weak. This strengthens the hypothesis that in some systems the formation of weights and topology obey different dynamics. Contrarily, we found strong coupling, both at the topological and weighted levels, even in networks that are not embedded in any obvious metric space like the metabolism of E. Coli, a system of metabolic reactions for which the hidden geometry is elucidated as a biochemical affinity space. This fact provides yet another empirical evidence towards the existence of hidden metric spaces shaping the architecture of these systems and, more generally, of real complex networks Serrano et al. (2008).
Our framework can be understood as a new generation of gravity models applicable to very different domains, including Biology, Information and Communication Technologies, and Social Systems. Indeed, Eq. (2) is a novel generalisation of this concept to the case of weighted networks where
plays the role of the “mass” of nodes and ensures that, once the network has been assembled, nodes have expected degree and strength and , respectively. Current gravity models predict the volume of flows between elements but cannot explain the observed topology of the interactions among them, as shown in works for the world trade web Dueñas and Fagiolo (2013). Our contribution overcomes this limitation and offers a gravity model that can reproduce both the existence and the intensity of interactions. This opens a new line of theoretic research on the coupling between topology, weighted structure, and geometry in complex networks.
Furthermore, our work opens the possibility to use information encoded in the weights of the links to find more accurate embeddings of real networks. Such improved embeddings are expected to allow the detection of communities or of missing links and to provide estimates of the weights of such missing links Liben-Nowell and Kleinberg (2007); Lü and Zhou (2010); Zhao et al. (2015). They can also be extremely helpful to implement navigation and searching protocols, such as greedy routing, which take into account not only the existence of connections but also their intensity.
In perspective, the hidden metric space weighted model and the maps of real complex systems that it will enable will lead to a deeper understanding of the interplay between the structure and function of real networks, and will offer insights on the impact they have on the dynamical processes they support and on their own evolutionary dynamics.
In addition to the details given in Table 1, we provide further information and references about the real complex networks used in this paper.
The world trade web describes significant trade exchanges between countries in 2013. The corresponding weights are trade volumes between pairs in USD García-Pérez et al. (2016).
The international network of global cargo ship movements consists of the number of shipping journeys between pairs of major commercial ports in the world in 2007 Kaluza et al. (2010).
The commodities network corresponds to the flows of the goods and services in millions of USD between industrial sectors in the United States in 2007Grady et al. (2012).
The airports network indicates the number of passengers that flew between pairs of airports in the United States in 2013. Data is freely available at the website of the U.S. Bureau of Transportation Statistics (transtats.bts.gov).
The commuting network reflects the daily flow of commuters between counties in the United States in 2000 Grady et al. (2012).
Weights in the metabolic network of the bacteria Escherichia Coli (E. Coli) K-12 MG1655 consist of the number of different metabolic reactions in which two metabolites participate Orth et al. (2011); Serrano et al. (2012).
Weights in the human brain network correspond to the density of anatomical connections between subregions of the human brain as detected via diffusion tensor imaging Avena-Koenigsberger et al. (2014).
Except for the metabolic and human brain networks, all networks were filtered using the disparity filter defined in Ref. Serrano et al. (2009) to preserve the most statistically significant connections. Many real weighted networks are generated from data by using a very broad definition of what constitutes a significant connection. This results in networks with huge average degrees and in which many links are noisy and weakly related to the overall functionality of the network. For instance, the US airports network contains links due to private flights (of the order of 10 passengers per year), which obviously follow different patterns of connection than the regular commercial airlines. Another interesting example is the world trade web, in which many trade interactions amount for less than one million dollars and are extremely volatile, appearing and disappearing from year to year. Indeed, it has been shown in Ref. García-Pérez et al. (2016) that removing these noisy connections yields a significantly more congruent topology with real economic factors, such as the gross domestic product (GDP).
The disparity quantifies the local heterogeneity of the weights attached to a given node and is defined as
where is the weight of the link between nodes and ( if there is no link) and Barthélemy et al. (2005). From this definition, we see that the disparity scales as whenever the weights are roughly homogeneously distributed among the links. Conversely, whenever the disparity decreases slower than implies that weights are heterogeneous and that the large strength of a node is due to a handful of links with large weights.
- Barrat et al. (2004a) A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani, ‘‘The architecture of complex weighted networks.” Proc. Natl. Acad. Sci. U. S. A. 101, 3747–52 (2004a).
- Serrano (2008) M. A. Serrano, “Rich-club vs rich-multipolarization phenomena in weighted networks,” Phys. Rev. E 78, 026101 (2008).
- Brockmann and Helbing (2013) D. Brockmann and D. Helbing, “The Hidden Geometry of Complex, Network-Driven Contagion Phenomena,” Science 342, 1337–1342 (2013).
- Newman (2010) Mark Newman, Networks: An Introduction (Oxford University Press, Inc., New York, NY, USA, 2010).
- Cohen and Havlin (2010) R. Cohen and S. Havlin, Complex Networks: Structure, Robustness and Function (Cambridge University Press, 2010).
- Serrano et al. (2008) M. Á. Serrano, D. Krioukov, and M. Boguñá, “Self-similarity of complex networks and hidden metric spaces,” Phys. Rev. Lett. 100, 078701 (2008).
- Boguñá and Krioukov (2009) M. Boguñá and D. Krioukov, “Navigating ultrasmall worlds in ultrashort time,” Phys. Rev. Lett. 102, 058701 (2009).
- Boguñá et al. (2009) M. Boguñá, D. Krioukov, and K. C. Claffy, “Navigability of complex networks,” Nat. Phys. 5, 74–80 (2009).
- Krioukov et al. (2010) D. Krioukov, F. Papadopoulos, M. Kitsak, A. Vahdat, and M. Boguñá, “Hyperbolic geometry of complex networks,” Phys. Rev. E 82, 036106 (2010).
- Gugelmann et al. (2012) L. Gugelmann, K. Panagiotou, and U. Peter, “Random hyperbolic graphs: Degree sequence and clustering,” in Automata, Languages, and Programming, Lecture Notes in Computer Science, Vol. 7392, edited by Artur Czumaj, Kurt Mehlhorn, Andrew Pitts, and Roger Wattenhofer (Springer Berlin Heidelberg, 2012) pp. 573–585.
- Bode et al. (2013) M. Bode, N. Fountoulakis, and T. Müller, “On the giant component of random hyperbolic graphs,” in The Seventh European Conference on Combinatorics, Graph Theory and Applications, CRM Series, Vol. 16, edited by Jaroslav Nešetřil and Marco Pellegrini (Scuola Normale Superiore, 2013) pp. 425–429.
- Candellero and Fountoulakis (2014) E. Candellero and N. Fountoulakis, “Clustering and the hyperbolic geometry of complex networks,” in Algorithms and Models for the Web Graph, Lecture Notes in Computer Science, Vol. 8882, edited by Anthony Bonato, Fan Chung Graham, and Paweł Pralat (Springer International Publishing, 2014) pp. 1–12.
- Friedrich and Krohmer (2015) T. Friedrich and A. Krohmer, “On the diameter of hyperbolic random graphs,” in Automata, Languages, and Programming, Lecture Notes in Computer Science, Vol. 9135, edited by Magnús M. Halldórsson, Kazuo Iwama, Naoki Kobayashi, and Bettina Speckmann (Springer Berlin Heidelberg, 2015) pp. 614–625.
- Aste et al. (2005) T. Aste, T. Di Matteo, and S. T. Hyde, “Complex networks on hyperbolic surfaces,” Physica A 346, 20–26 (2005).
- Papadopoulos et al. (2012) F. Papadopoulos, M. Kitsak, M. Á. Serrano, M. Boguñá, and D. Krioukov, “Popularity versus similarity in growing networks,” Nature 489, 537–540 (2012).
- Gulyás et al. (2015) A. Gulyás, J. J. Bíró, A. Kőrösi, G. Rétvári, and D. Krioukov, “Navigable networks as Nash equilibria of navigation games,” Nat. Commun. 6, 7651 (2015).
- Boguñá et al. (2010) M. Boguñá, F Papadopoulos, and D. Krioukov, ‘‘Sustaining the Internet with hyperbolic mapping,” Nat. Commun. 1, 62 (2010).
- Serrano et al. (2012) M. Á. Serrano, M. Boguñá, and F. Sagués, “Uncovering the hidden geometry behind metabolic networks,” Mol. Biosyst. 8, 843–850 (2012).
- García-Pérez et al. (2016) G. García-Pérez, M. Boguñá, A. Allard, and M. Á. Serrano, “The hidden hyperbolic geometry of international trade: World Trade Atlas 1870-2013,” Sci. Rep. 6, 33441 (2016).
- Popović et al. (2012) M. Popović, H. Štefančić, and V. Zlatić, “Geometric Origin of Scaling in Large Traffic Networks,” Phys. Rev. Lett. 109, 208701 (2012).
- Barthélemy (2011) M. Barthélemy, “Spatial networks,” Phys. Rep. 499, 1–101 (2011).
- Pajevic and Plenz (2012) S. Pajevic and D. Plenz, “The organization of strong links in complex networks,” Nat. Phys. 8, 429–436 (2012).
- Bianconi (2004) G. Bianconi, “Emergence of weight-topology correlations in complex scale-free networks,” Europhys. Lett. 71, 1029–1035 (2004).
- Barrat et al. (2004b) A. Barrat, M. Barthélemy, and A. Vespignani, “Weighted Evolving Networks: Coupling Topology and Weight Dynamics,” Phys. Rev. Lett. 92, 228701 (2004b).
- Yook et al. (2001) S. Yook, H. Jeong, A.-L. Barabási, and Y. Tu, “Weighted Evolving Networks,” Phys. Rev. Lett. 86, 5835–5838 (2001).
- Kumpula et al. (2007) J. M. Kumpula, J.-P. Onnela, J. Saramäki, K. Kaski, and J. Kertész, “Emergence of Communities in Weighted Networks,” Phys. Rev. Lett. 99, 228701 (2007).
- Antal and Krapivsy (2005) T. Antal and P. L. Krapivsy, “Weight-driven growing networks,” Phys. Rev. E 71, 026103 (2005).
- Zheng et al. (2003) D. Zheng, S. Trimper, B. Zheng, and P. M. Hui, “Weighted Scale-Free Networks with Stochastic Weight Assignments,” Phys. Rev. E 67, 040102(R) (2003).
- Wang et al. (2005) W.-X. Wang, B. Hu, T. Zhou, B.-H. Wang, and Y.-B. Xie, “Mutual selection model for weighted networks,” Phys. Rev. E 72, 046140 (2005).
- Li et al. (2006) M. Li, D. Wang, Y. Fan, Z. Di, and J. Wu, “Modelling weighted networks using connection count,” New J. Phys. 8, 72 (2006).
- Mastrandrea et al. (2014) R. Mastrandrea, T. Squartini, G. Fagiolo, and D. Garlaschelli, “Enhanced reconstruction of weighted networks from strengths and degrees,” New J. Phys. 16, 043022 (2014).
- Garlaschelli (2009) D. Garlaschelli, “The weighted random graph model,” New J. Phys. 11, 073005 (2009).
- Garlaschelli and Loffredo (2009) D. Garlaschelli and M. Loffredo, “Generalized Bose-Fermi Statistics and Structural Correlations in Weighted Networks,” Phys. Rev. Lett. 102, 038701 (2009).
- Sagarra et al. (2013) O. Sagarra, C. Pérez Vicente, and A. Díaz-Guilera, “Statistical mechanics of multiedge networks,” Phys. Rev. E 88, 062806 (2013).
- Sagarra et al. (2015) O. Sagarra, C. J. Pérez Vicente, and A. Díaz-Guilera, “Role of adjacency-matrix degeneracy in maximum-entropy-weighted network models,” Phys. Rev. E 92, 052816 (2015).
- Papadopoulos et al. (2015) F. Papadopoulos, R. Aldecoa, and D. Krioukov, “Network geometry inference using common neighbors,” Phys. Rev. E 92, 022807 (2015).
- Dueñas and Fagiolo (2013) M. Dueñas and G. Fagiolo, “Modeling the International-Trade Network: a gravity approach,” J. Econ. Interact. Coord. 8, 155–178 (2013).
- Liben-Nowell and Kleinberg (2007) David Liben-Nowell and Jon Kleinberg, “The link-prediction problem for social networks,” J. Am. Soc. Inf. Sci. Technol. 58, 1019–1031 (2007).
- Lü and Zhou (2010) Linyuan Lü and Tao Zhou, “Link prediction in weighted networks: The role of weak ties,” EPL (Europhysics Letters) 89, 18001 (2010).
- Zhao et al. (2015) Jing Zhao, Lili Miao, Jian Yang, Haiyang Fang, Qian-Ming Zhang, Min Nie, Petter Holme, and Tao Zhou, “Prediction of links and weights in networks by reliable routes,” Scientific Reports 5, 12261 (2015).
- Kaluza et al. (2010) P. Kaluza, A. Kölzsch, M. T. Gastner, and B. Blasius, “The complex network of global cargo ship movements.” J. R. Soc. Interface 7, 1093–1103 (2010).
- Grady et al. (2012) Daniel Grady, Christian Thiemann, and Dirk Brockmann, “Robust classification of salient links in complex networks,” Nat. Commun. 3, 864 (2012).
- Orth et al. (2011) J. D. Orth et al., “A comprehensive genome-scale reconstruction of Escherichia coli metabolism - 2011,” Mol. Syst. Biol. 7, 535 (2011).
- Avena-Koenigsberger et al. (2014) A. Avena-Koenigsberger, J. Goni, R. F. Betzel, M. P. van den Heuvel, A. Griffa, P. Hagmann, J.-P. Thiran, and O. Sporns, “Using Pareto optimality to explore the topology and dynamics of the human connectome,” Philos. Trans. R. Soc. B Biol. Sci. 369, 20130530 (2014).
- Serrano et al. (2009) M. Á. Serrano, M. Boguñá, and A. Vespignani, “Extracting the multiscale backbone of complex weighted networks,” Proc. Natl. Acad. Sci. USA 106, 6483–6488 (2009).
- Barthélemy et al. (2005) M. Barthélemy, A. Barrat, R. Pastor-Satorras, and A. Vespignani, “Characterization and modeling of weighted networks,” Physica A 346, 34–43 (2005).
Acknowledgements.We acknowledge support from the James S. McDonnell Foundation Scholar Award in Complex Systems; the Fonds de recherche du Québec – Nature et technologies; the ICREA Academia prize, funded by the Generalitat de Catalunya; the MINECO project no.FIS2013-47282-C2-1-P; and the Generalitat de Catalunya grant no.2014SGR608.
Author contributions: A.A., M.Á.S., G.G.-P., and M.B. contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript.
Competing financial interests: The authors declare no competing financial interests.
Data availability: Codes and data supporting the findings of this study are available from the corresponding author upon request. See pages 1 of SuppInfo.pdf See pages 2 of SuppInfo.pdf See pages 3 of SuppInfo.pdf See pages 4 of SuppInfo.pdf See pages 5 of SuppInfo.pdf See pages 6 of SuppInfo.pdf See pages 7 of SuppInfo.pdf See pages 8 of SuppInfo.pdf See pages 9 of SuppInfo.pdf See pages 10 of SuppInfo.pdf See pages 11 of SuppInfo.pdf See pages 12 of SuppInfo.pdf See pages 13 of SuppInfo.pdf See pages 14 of SuppInfo.pdf See pages 15 of SuppInfo.pdf See pages 16 of SuppInfo.pdf See pages 17 of SuppInfo.pdf See pages 18 of SuppInfo.pdf See pages 19 of SuppInfo.pdf See pages 20 of SuppInfo.pdf See pages 21 of SuppInfo.pdf See pages 22 of SuppInfo.pdf See pages 23 of SuppInfo.pdf See pages 24 of SuppInfo.pdf See pages 25 of SuppInfo.pdf See pages 26 of SuppInfo.pdf See pages 27 of SuppInfo.pdf See pages 28 of SuppInfo.pdf See pages 29 of SuppInfo.pdf See pages 30 of SuppInfo.pdf See pages 31 of SuppInfo.pdf See pages 32 of SuppInfo.pdf See pages 33 of SuppInfo.pdf