Networks are mathematical structures that are universally used to describe a large variety of complex systems such as the brain or the Internet. Characterizing the geometrical properties of these networks has become increasingly relevant for routing problems, inference and data mining. In real growing networks, topological, structural and geometrical properties emerge spontaneously from their dynamical rules. Nevertheless we still miss a model in which networks develop an emergent complex geometry. Here we show that a single two parameter network model, the growing geometrical network, can generate complex network geometries with non-trivial distribution of curvatures, combining exponential growth and small-world properties with finite spectral dimensionality. In one limit, the non-equilibrium dynamical rules of these networks can generate scale-free networks with clustering and communities, in another limit planar random geometries with non-trivial modularity. Finally we find that these properties of the geometrical growing networks are present in a large set of real networks describing biological, social and technological systems.
Recently, in the network science community1,2,3,4, the interest in the geometrical characterizations of real network datasets has been growing. This problem has indeed many applications related to routing problems in the Internet5,6,7,8, data mining and community detection9,10,11,12,13,14. At the same time, different definitions of network curvatures have been proposed by mathematicians15,16,17,18,19,20,21,22,23,24 and the characterization of the hyperbolicity of real network datasets has been gaining momentum thanks to the formulation of network models embedded in hyperbolic planes25,26,27,28,29 and by the definition of delta hyperbolicity of networks by Gromov22,30–32. This debate on geometry of networks includes also the discussion of useful metrics for spatial networks33,34 embedded into a physical space and its technological application including wireless networks35.
In the apparently unrelated field of quantum gravity, pregeometric models, where space is an emergent property of a network or of a simplicial complex, have attracted large interest over the years36,37,38,39,40,41,42,43. Whereas in the case of quantum gravity the aim is to obtain a continuous spacetime structure at large scales, the underlying simplicial structure from which geometry should emerge bears similarities to networks. Therefore we think that similar models taylored more specifically to our desired network structure (especially growing networks) could develop emergent geometrical properties as well.
Here our aim is to propose a pregeometric model for emergent complex network geometry, in which the non-equilibrium dynamical rules do not take into account any embedding space, but during its evolution the network develops a certain heterogeneous distribution of curvatures, a small-world topology characterized by high clustering and small average distance, a modular structure and a finite spectral dimension.
In the last decades the most popular framework for describing the evolution of complex systems has been the one of growing network models1,2,3. In particular growing complex networks evolving by the preferential attachment mechanism have been widely used to explain the emergence of the scale-free degree distributions which are ubiquitous in complex networks. In this scenario, the network grows by the addition of new nodes and these nodes are more likely to link to nodes already connected to many other nodes according to the preferential attachment rule. In this case the probability that a node acquires a new link is proportional to the degree of the node. The simplest version of these models, the Barabasi-Albert (BA) model44, can be modified1,2,3 in order to describe complex networks that also have a large clustering coefficient, another important and ubiquitous property of complex networks that characterizes small-world networks45 together with the small typical distance between the nodes. Moreover, it has been recently observed46,47 that growing network models inspired by the BA model and enforcing a high clustering coefficient, using the so called triadic closure mechanism, are able to display a non trivial community structure48,49. Finally, complex social, biological and technological networks not only have high clustering but also have a structure which suggests that the networks have an hidden embedding space, describing the similarity between the nodes. For example the local structure of protein-protein interaction networks, analysed with the tools of graphlets, suggests that these networks have an underlying non-trivial geometry50,51.
Another interesting approach to complex networks suggests that network models evolving in a hyperbolic plane might model and approximate a large variety of complex networks28,29. In this framework nodes are embedded in a hidden metric structure of constant negative curvature that determine their evolution in such a way that nodes closer in space are more likely to be connected.
But is it really always the case that the hidden embedding space is causing the network dynamics or might it be that this effective hidden metric space is the outcome of the network evolution?
Here we want to adopt a growing network framework in order to describe the emergence of geometry in evolving networks. We start from non-equilibrium growing dynamics independent of any hidden embedding space and we show that spatial properties of the network emerge spontaneously. These networks are the skeleton of growing simplicial complexes that are constructed by gluing together simplices of given dimension. In particular in this work we focus on simplicial complexes built by gluing together triangles and imposing that the number of triangles incident to a link cannot be larger than a fixed number that parametrizes the network dynamics. In this way we provide evidence that the proposed stylized model, including only two parameters, can give rise to a wide variety of network geometries and can be considered a starting point for characterizing emergent space in complex networks. Finally we compare the properties of real complex system datasets with the structural and geometric properties of the growing geometrical model showing that despite the fact that the proposed model is extremely stylized, it captures main features observed in a large variety of datasets.
Metric spaces satisfy the triangular inequality. Therefore in spatial networks we must have that if a node connects two nodes (the node and the node ), these two must be connected by a path of short distance. Therefore, if we want to describe the spontaneous emergence of a discrete geometric space, in absence of an embedding space and a metric, it is plausible that starting from growing simplicial complexes should be an advantage. These structures are formed by gluing together complexes of dimension , i.e. fully connected networks, or cliques, formed by nodes, such as triangles, tetrahedra etc. For simplicity, let us here consider growing networks constructed by addition of connected complexes of dimension , i.e. triangles. We distinguish between two cases: the case in which a link can belong to an arbitrarily large number of triangles () and the case in which each link can belong at most to a finite number of triangles. In the case in which is finite we call the links to which we can still add at least one triangle unsaturated. All the other links we call saturated.
To be precise, we start from a network formed by a single triangle, a simplex of dimension . At each time we perform two processes (see Fig. 1).
• Process (a)- We add a triangle to an unsaturated link of the network linking node to node . We choose this link randomly with probability given by
where is the element of the adjacency matrix a of the network and where the matrix element is equal to one (i.e. ) if the number of triangles to which the link belongs is less than , otherwise it is zero (i.e. ). Having chosen the link we add a node , two links and and the new triangle linking node , node and node .
• Process (b)- With probability we add a single link between two nodes at hopping distance and we add all the triangles that this link closes, without adding more than triangles to each link. In order to do this, we choose an unsaturated link with probability given by Eq. (1), then we choose one random unsaturated link adjacent either to node or node as long as this link is not already part of a triangle including node and node . Therefore we choose the link with probability given by
where is the Kronecker delta and is the normalization constant. Let us assume without loss of generality that the chosen link . Then we add a link and all the triangles passing through node and node as long as this process is allowed (i.e. if by doing so we do not add more than triangles to each link). Otherwise we do nothing.
With the above algorithm (see Supplementary Information for the MATLAB code) we describe a growing simplicial complex formed by adding triangles. From this structure we can extract the corresponding network where we consider only the information about node connectivity (which node is linked to which other node). We call this network model the geometrical growing network. In Fig. 1 we show schematically the dynamical rules for building the growing simplicial complexes and the geometrical growing networks that describe its skeleton.
Let us comment on two fundamental limits of this dynamics. In the case , , the network is scale-free and in the class of growing networks with preferential attachment. In fact the probability that we add a link to a generic node of the network using process is simply proportional to the number of links connected to it, i.e. its degree . Therefore, the mean-field equations for the degree of a generic node are equal to the equations valid for the BA model, i.e. they yield a scale-free network with power-law exponent . Actually this limit of our model was already discussed in52 as a simple and major example of scale-free network. For , instead, the degree distribution can be shown to be exponential (see Methods and Supplementary material for details). The Euler characteristic of our simplicial complex and the corresponding network is given by
where indicates the total number of nodes, the total number of links and the total number of triangles in the network. For and any value of , or for and any value of the networks are planar graphs since the non-planar subgraphs (complete graph of five nodes) and (complete bipartite graph formed by two sets of three nodes) are excluded from the dynamical rules (see Methods for details). Therefore in these cases we have an Euler characteristic (in fact here we do not count the external face).
In general the proposed growing geometric network model can generate a large variety of network geometries. In Fig. 2 we show a visualization of single instances of the growing geometrical networks in the cases , (random planar geometry), , (scale-free geometry) and ,
The growing geometrical network model has just two parameters and . The role of the parameter is to fix the maximal number of triangles incident on each link. The role of the parameter is to allow for a non-trivial K-core structure of the network. In fact, if the network can be completely pruned if we remove nodes of degree recursively, similarly to what happens in the BA model, while for the geometrical growing network has a non-trivial -core. Moreover the process can be used to “freeze” some region of the network. In order to see this, let us consider the role of the process occurring with probability in the case of a network with . Then for , each node will increase its connectivity indefinitely with time having always exactly two unsaturated links attached to it. On the contrary, if there is a small probability that some nodes will have all adjacent links saturated and a degree that is frozen and does not grow any more. A typical network of this type is shown for in Fig. 2 where one can clearly distinguish between an active boundary of the network where still many triangles can be linked and a frozen bulk region of the network.
The geometrical growing networks have highly heterogeneous structure reflected in their local properties. For example, the degree distribution is scale-free for and exponential for for any value of . Moreover for finite values of the degree distribution can develop a tail that is broader for increasing values of (see Fig. 3). Furthermore, in Fig. 3 we plot the average clustering coefficient of nodes of degree showing that the geometrical growing networks are hierarchical49, they have a clustering coefficient with values of that are typically .
Another important and geometrical local property is the curvature, defined on each node of the network. For either and any value of or for and any value of , the generated graph is a planar network of which all faces are triangles. Therefore we consider the curvature 19,20,21,22 given by
where is the degree of node and is the number of triangles passing through node .
We observe that the definition of the curvature satisfies the Gauss-Bonnet theorem
For a planar network, for bulk nodes which have the curvature reduces to
and for nodes at the boundary for which , it reduces to
Note that the expression in Eq. (7) is also valid for as long as . In fact for these networks only process takes place and it is easy to show that . This simple relation between the curvature and the degree allows to characterize the distribution of curvatures in the network easily. The curvature is intuitively related to the degree of the node. As all triangles are isosceles, a bulk node with degree six has zero curvature. In fact the sum of the angles of the triangles incident to the node is . Otherwise the sum is smaller or larger than resulting in positive or negative curvature respectively. The argument works similarly for the nodes at the boundary.
For and the networks are not planar anymore and the definition of curvature is debated 15,16,17,18. Here we decided to continue to use the definition given by Eq. (4). This is equivalent to the definition of curvature by Oliver Knill23,24, in which the curvature at a node is defined as
where are the number of simplices of nodes and dimension to which node belongs. In fact the definition of curvature given by Eq. (4) is equivalent to the definition given by Eq. (8) if we truncate the sum in Eq. (8) to simplices of dimension , i.e. we consider only nodes, links and triangles since these are the original simplices building our network.
For the curvature distribution is dominated by a negative unbounded tail that is exponential in the case and power-law in the case . In particular while the average curvature is for and any value of , in the limit the fluctuations around this average are finite (i.e. ) for and infinite (i.e. ) for . We note here that in the BA model the clustering coefficient of any node vanishes in the large network limit, therefore the curvature and the curvature distribution has a power-law negative tail and diverging in the large network limit, similarly to the case and of the present model.
For a general value of , we can assume that the average clustering of nodes of degree , scales as . Then the average number of triangles of nodes of degree , scales as . Therefore, for large and as long as the average curvature of nodes of degree , is dominated by the contribution of triangles and scales like with a positive tail for large values of . This allows us to distinguish the phase diagram in two different regions according to the value of the exponent : the case in which the curvature has a positive tail and the case in which the curvature can have a negative tail.
We make here two main observations. First of all, with the definition of the curvature given byEq. (4), our network model has heterogeneous distribution of curvatures. Therefore here we are characterizing highly heterogeneous geometries and the geometrical growing network does not have a constant curvature. This is one of the main differences of the present model compared to network models embedded in the hyperbolic plane28,29. In particular all the networks with or have and therefore the average curvature is zero in the thermodynamical limit, but they have a curvature distribution with an unbounded negative tail that can be either exponential for (i.e. ) or scale-free as for the case (i.e. ).
We illustrate this in Fig. 3 where we plot the distribution of curvatures for different specific models of growing geometrical networks for and for different values of . We show that for the negative tail can be either exponential or scale-free. For we have for a negative exponential tail and for a positive scale-free tail of the curvature distribution consistent with a value of the exponent and a power-law degree distribution.
Our second observation is that the case and is significantly different from the case and . In fact for and for the Euler characteristic of the network is and never increases in time (see Methods for details), while for the case , we expect to go to a finite limit as goes to infinity. In Fig. 4 the numerical results of the Euler characteristic as a function of the network size shows that, for and , grows linearly with . The quantity gives the average curvature in the network and is therefore zero for and .
The generated topologies are small-world. In fact they combine high clustering coefficient with a typical distance between the nodes increasing only logarithmically with the network size. The exponential growth of the network is to be expected by the observation that in these networks we always have that the total number of links as well as the number of unsaturated links scale linearly with time. This corresponds to a physical situation in which the “volume” (total number of links) is proportional to the “surface” (number of unsaturated links). Therefore we should expect that the typical distance of the nodes in the network should grow logarithmically with the network size . In order to check this, in Fig. 4 we give , the average distance of the nodes from the initial triangle over the different network realisations as a function of the network size . From this figure it is clear that asymptotically in time , independently of the value of and .
The effects of randomness and emergent locality in these networks are reflected by their cluster structure, revealed by the lower bound on their maximal modularity measured by running efficient community detection algorithms53 (Fig. 5). Moreover also their clustering coefficient provides evidence for their emergent locality (Fig. 5). Finally we observe that for the network develops also a non-trivial K-core structure. In order to show this in Fig. 5 we also plot the value of corresponding to the maximal -core of the network. As we already mentioned, for we have and the network can be completely pruned by removing the triangles recursively. For instead, the maximal -core can have a much larger value of , as shown in Fig. 5 for a network of nodes.
Therefore these structures are different from the small world model to the extent that they are always characterised by a non-trivial community and -core structure.
The geometrical growing network is growing exponentially, so the Hausdorff dimension is infinite. Nevertheless, these networks develop a finite spectral dimension as clearly shown in Fig. 6, for and . We have checked that also for other values of the spectral dimension remains finite. This is a clear indication that these networks have non-trivial diffusion properties.
The geometrical growing network model is therefore a very stylized model with interesting limiting behaviour, in which geometrical local and global parameters can emerge spontaneously from the non-equilibrium dynamics. Moreover here we compare the properties of the geometric growing network with the properties of a variety of real datasets. In particular we have considered network datasets coming from biological, social and technological systems and we have analysed their properties. In Table 1 we show that in several cases large modularity, large clustering, small average distance and non-trivial maximal -core structure emerge. Moreover, in these datasets a non-trivial distribution of curvature (defined as in Eq. (4)) is present, showing either negative or positive tail (see Fig. 7). Finally the Laplacian spectrum of these networks also displays a power-law tail from which an effective finite spectral dimension can be calculated (see Table 1 and Supplementary Information for details). This shows that the geometrical growing network models have many properties in common with real datasets, describing biological, social and technological systems and should therefore be used and modified to model several real network datasets.
In conclusion, this paper shows that growing simplicial complexes and the corresponding growing geometrical networks are characterized by the spontaneous emergence of locality and spatial properties. In fact small-world properties, non-trivial community structure and even finite spectral dimensions are emerging in these networks despite the fact that their dynamical rules do not depend on any embedding space. These growing networks are determined by non-equilibrium stochastic dynamics and provide evidence that it is possible to generate random complex self-organized geometries by simple stochastic rules.
An open question in this context is to determine the underlying metric for these networks. In particular we believe that the investigation of the hyperbolic character of the models with and (that have zero average curvature but a negative third moment of the distribution of curvature) should be extremely interesting to shed new light on “random geometries” in which the curvature can have finite or infinite deviations from its average. A full description of their structure using tools of geometric group theory could be envisaged to solve this problem. This analysis could be facilitated also by the study of the dual network in which each triangle is a node of maximal degree . In fact each edge of the triangle is at most incident to other triangles in the geometrical growing network.
Furthermore we mention that the model can be generalized in two main directions. On the one hand the model can be extended by considering geometrical growing networks built by gluing together simplices of higher dimension. On the other hand, one can explore methods to generate networks that have a finite Hausdorff dimension, i.e. that they have a typical distance between the nodes scaling like a power of the total number of nodes in the network. Another interesting direction of further theoretical investigation is to consider the equilibrium models of networks (ensembles of networks) in which a constraint on the total number of triangles incident to a link is imposed, similarly to recent works that have considered ensembles with given degree correlations and average clustering coefficient of nodes of degree 54.
Finally the geometrical growing network is a very stylized model and includes the essential ingredients for describing the emergence of locality of the interactions in complex networks and can be used in a variety of fields in which networks and discrete spaces are important, including complex networks with clustering such as biological, social and technological networks.
Degree distribution of and -
In the case and the geometrical growing network model is reduced to the model proposed in52. Here we show the derivation of the scale-free distribution in this case for completeness. In the geometrical growing network with and at each time a random link is chosen and a new node attaches two links to the two ends of it. Therefore the probability that at time a new link is attached to a given node of degree is given by . Using this result we can easily write the master equation for the number of nodes of degree at time ,
Since the network is growing, asymptotically in time the number of nodes of degree will be proportional to the degree distribution , , where the total number of nodes in the network is . Therefore, substituting this scaling in Eq. (9) we get
for every , while yielding the solution
for , which is equal to the degree distribution of the BA model with minimal degree equal to , i.e. scale-free with power-law exponent . Here we observe that the curvature of the nodes is in this case , therefore has a power-law negative tail, i.e. for and . Moreover we have (consistent with ) but is diverging with the network size .
Degree distribution of for -
The degree distribution for is exponential for any value of . Here we discuss the simple case leaving the treatment of the case to the Supplementary Information. For every node has exactly two unsaturated links. The total number of unsaturated links is at large time . Therefore the average number of links that a node gains at time by process is given by for . The master equations for the average number of nodes that have degree at time are given by
In the large time limit, in which , the degree distribution is given by
for . The curvature is therefore in average in the limit with finite second moment .
Euler characteristic of geometrical growing network with either or -
The Euler characteristic of the geometrical growing networks with is at every time. In fact we start from a single triangle, therefore at we have . At each time step we attach a new triangle to a given unsaturated link, therefore we add one new node, two new links and one new triangle, so that . Hence for every network size. For also the process does not increase the Euler characteristic. In fact in this case when the process occurs and , we add only one new link and one new triangle, therefore also for this process. Instead in the case and , process always adds a single link but the number of triangles that close is in average greater than one, therefore the Euler characteristic grows linearly with the network size .
Definition of Modularity -
The modularity is a measure to evaluate the significance of the community structure of a network. It is defined48 as
Here, denotes the adjacency matrix of the network, the total number of links and , where , indicates to which community the node belongs. Finding the network partition that optimizes modularity is a NP hard problem. Therefore different greedy algorithms have been proposed to find the community structure such as the Leuven method53 that we have used in this study. The modularity found in this way is a lower bound on the maximal modularity of the network.
Definition of the Clustering coefficient-
The clustering coefficient is given by the probability that two nodes, both connected to a common node, are also connected. In the context of social networks, it describes the probability that a friend of a friend is also your friend. The local clustering coefficient of node has been defined as the probability that two neighbours of the node are neighbours of each other,
where is the number of triangles passing through node and is the degree of node .
Definition of the -core-
We define the -core of a network as the maximal subgraph formed by the set of nodes that have at least links connecting them to the other nodes of the -core. The -core of a network can be easily obtained by pruning a given network, i.e. by removing iteratively all the nodes with degree .
Definition of the spectral dimension of a network-
The Laplacian matrix of the network has elements
If the density of eigenvalues of the Laplacian scales like
with , for small values of , then is called the spectral dimension of the network. For regular lattices in dimension we have . Clearly, if the spectral dimension of a network is well defined, then the cumulative distribution scales like
for small values of .
We analysed a large variety of biological, technological and social datasets. In particular we have considered the brain network of co-activation5555, 4 protein contact maps58 (see Supplementary Information for details on the data analysis), the Internet at the Autonomous System level56, the US power-grid45 and a social network of friendship between high-school students coming from the Add Health dataset AddHealth.
How to cite this article: Wu, Z. et al. Emergent Complex Network geometry. Sci. Rep. 5, 10073; doi: 10.1038/srep10073 (2015).
Albert, R. & Barabási, A. - L. Statistical mechanics of complex networks. Rev. of Mod. Phys. 74, 47 (2002).
Newman, M. E. J. Networks: An introduction. Oxford University Press, Oxford, 2010).
Dorogovtsev, S. N. & Mendes, J. F. F. Evolution of networks: From biological nets to the Internet and WWW Oxford University Press, Oxford, 2003).
Fortunato, S. Community detection in graphs. Phys. Rep. 486, 75 (2010).
Kleinberg, R. Geographic routing using hyperbolic space. In INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE, 1902, (2007).
Boguñá, M., Krioukov, D. & Claffy, K. C. Navigability of complex networks. Nature Physics 5, 74 (2008).
Boguñá, M., Papadopoulos, F. & Krioukov, D. Sustaining the internet with hyperbolic mapping. Nature Commun. 1, 62 (2010).
Narayan, O. & Saniee, I. Large-scale curvature of networks. Phys. Review E 84, 066108 (2011).
Leskovec, J., Lang, K. J., Dasgupta, A. & Mahoney, M. W. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics 6, 29 (2009).
Adcock, A. B., Sullivan, B. D. & Mahoney, M. W. Tree-like structure in large social and information networks. In Data Mining (ICDM), 2013 IEEE 13th International Conference on, 1. IEEE, (2013).
Petri, G. Scolamiero, M., Donato, I. & Vaccarino F. Topological strata of weighted complex networks. PloS One 8, e66506 (2013).
Petri, G., et al. Homological scaffolds of brain functional networks. Journal of The Royal Society Interface 11, 20140873 (2014).
Donetti, L. & Munoz, M. A. Detecting network communities: a new systematic and efficient algorithm. Journal of Statistical Mechanics: Theory and Experiment P10012 (2004).
Cao, X., Wang, X., Jin, D., Cao, Y. & He D. Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization. Sci. Rep. 3, 2993 (2013).
Lin, Y., Lu, L. & Yau, S.-T. Ricci curvature of graphs. Tohoku Mathematical Journal 63, 605 (2011).
Lin, Y. & Yau, S.-T. Ricci curvature and eigenvalue estimate on locally finite graphs. Math. Res. Lett 17 343 (2010).
Bauer, F. J. Jost, J. & Liu, S. Ollivier-Ricci curvature and the spectrum of the normalized graph Laplace operator. arXiv preprint arXiv:1105.3803 (2011).
Ollivier, Y. Ricci curvature of Markov chains on metric spaces. Journal of Functional Analysis 256, 810 (2009).
Keller, M., Curvature, geometry and spectral properties of planar graphs. Discrete & Computational Geometry 46, 500 (2011).
Keller, M. & Norbert P., Cheeger constants, growth and spectrum of locally tessellating planar graphs. Mathematische Zeitschrift 268, 871 (2011).
Higuchi, Y., Combinatorial curvature for planar graphs. Journal of Graph Theory 38, 220 (2001).
Gromov, M. Hyperbolic groups Springer, New York, 1987).
Knill, O. On index expectation and curvature for networks. arXiv preprint arXiv:1202.4514 (2012).
Knill, O. A discrete Gauss-Bonnet type theorem. arXiv preprint arXiv:1009.2292 (2010).
Nechaev, S. & Voituriez R. On the plant leaf’s boundary, ‘jupe á godets’ and conformal embeddings. Journal of Physics A: Mathematical and General 34, 11069 (2001).
Nechaev, S. K. & Vasilyev, O.A. On metric structure of ultrametric spaces. Journal of Physics A: Mathematical and General 37, 3783 (2004).
Aste, T., Di Matteo, T. & Hyde, S. T. Complex networks on hyperbolic surfaces. Physica A: Statistical Mechanics and its Applications 346, 20 (2005).
Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A. & Boguñá, M. Hyperbolic geometry of complex networks. Phys. Rev. E 82 036106 (2010).
Papadopoulos, F., Kitsak, M., Serrano, M. A., Boguñá, M. & Krioukov, D. Popularity versus similarity in growing networks. Nature 489, 537 (2012).
Chen, W., Fang, W., Hu, G. & Mahoney, M. W. On the hyperbolicity of small-world and treelike random graphs. Internet Mathematics 9, 434 (2013).
Jonckheere, E., Lohsoonthorn, P. & Bonahon, F. Scaled Gromov hyperbolic raphs. Journal of Graph Theory 57, 157 (2008).
Jonckheere, E., Lou, M., Bonahon, F. & Baryshnikov, Y. Euclidean versus hyperbolic congestion in idealized versus experimental networks. Internet Mathematics 7, 1 (2011).
Barthélemy, M. Spatial networks. Phys. Rep. 499, 1 (2011).
Daqing, L., Kosmidis, K., Bunde, A. & Havlin, S. Dimension of spatially embedded networks. Nature Physics 7, 481 (2011).
Zeng, W., Sarkar, R., Luo, F., Gu, X. & Gao J. Resilient routing for sensor networks using hyperbolic embedding of universal covering space. In INFOCOM, 2010 Proceedings IEEE, 1, (2010).
Ambjorn, J., Jurkiewicz, J. & Loll R. Reconstructing the universe. Phys. Rev. D 72, 064014 (2005).
Ambjorn, J., Jurkiewicz, J. & Loll R. Emergence of a 4D world from causal quantum gravity. Phys. Rev. Lett. 93, 131301 (2004).
Wheeler, J. A. Pregeometry: Motivations and prospects. Quantum theory and gravitation ed. A. R. Marlov, Academic Press, New York, 1980).
Gibbs, P. E. The small scale structure of space-time: A bibliographical review. arXiv preprint hep-th/9506171 (1995).
Meschini, D., Lehto, M. & Piilonen, J. Geometry, pregeometry and beyond. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 36, 435 (2005).
Antonsen, F. Random graphs as a model for pregeometry. International journal of theoretical physics, 33, 11895 (1994).
Konopka, T. Markopoulou, F. & Severini, S. Quantum graphity: a model of emergent locality. Phys. Rev. D 77, 104029 (2008).
Krioukov, D., et al. Network Cosmology, Sci. Rep., 2 793 (2012).
Barabási, A.-L. & Albert, R., R. Emergence of scaling in random networks. Science 286, 509 (1999).
Watts, D. J. & Strogatz, S. H. Collective dynamics of “small-world” networks. Nature 393, 440 (1998).
Bianconi, G., Darst, R. K., Iacovacci, J. & Fortunato, S., Triadic closure as a basic generating mechanism of communities in complex networks. Phys. Rev. E 90, 042806 (2014).
Bhat, U., Krapivsky, P. L. & Redner, S.,Emergence of clustering in an acquaintance model without homophily. Journal of Statistical Mechanics: Theory and Experiment P11035 (2014).
Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004).
Ravasz, E. Somera, A. L., Mongru, D. A. Oltvai, Z. N. & Barabási, A.-L. Hierarchical organization of modularity in metabolic networks. Science 297, 1551 (2002).
Kuchaiev, O., Rasajski, M., Higham, D. J. & Przulj, N. Geometric de-noising of protein-protein interaction networks. PLoS Computational Biology 5, e1000454 (2009).
Przulj, N. Biological network comparison using graphlet degree distribution. Bioinformatics 23, e177 (2007).
Dorogovtsev, S. N., Mendes, J. F. F. & Samukhin A. N. Size-dependent degree distribution of a scale-free growing network. Phys. Rev. E 63, 062101 (2001).
Blondel, V. D., Guillaume, J. L. Lambiotte, R. & Lefebvre E. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, P10008 (2008).
Colomer-de-Simon, P., Serrano, M. A., Beiró, M. G. Alvarez-Hamelin, J.I. & Boguñá, M. Deciphering the global organization of clustering in real complex networks. Sci. Rep. 3, 2517 (2013).
Crossley, N. A., et al. Cognitive relevance of the community structure of the human brain functional coactivation network. Proceedings of the National Academy of Sciences 110, 11583 (2013).
Add Health Data, http://www.cpc.unc.edu/projects/addhealth/data (Date of access 10/11/2014).
Protein Data Bank, http://pdb.org/pdb/explore/explore.do?structureId=1L8W, http://pdb.org/pdb/explore/explore.do?structureId=1PHP, http://pdb.org/pdb/explore/explore.do?structureId=1QOP (Date of access 10/11/2014).
M.E. J. Newman, Internet at the level of autonomous systems reconstructed from BGP tables posted by the University of Oregon Route Views Project by M. E. Newman, http://www-personal.umich.edu/mejn/netdata/ (Date of access 10/11/2014).
Burioni, R., Cassi, D. Cecconi, F. & Vulpiani, A. Topological thermal instability and length of proteins. Proteins: Structure, Function and Bioinformatics 55, 529 (2004).
We acknowledge interesting discussions with Marián Boguñá, Oliver Knill and Sergei Nechaev. This work has been supported by the National Natural Science Foundation of China (61403023).Z. W. acknowledges the kind hospitality of the School of Mathematical Sciences at QMUL.
The authors declare no competing financial interests.
Electronic supplementary material
About this article
Cite this article
Wu, Z., Menichetti, G., Rahmede, C. et al. Emergent Complex Network Geometry. Sci Rep 5, 10073 (2015). https://doi.org/10.1038/srep10073
Brain Structure and Function (2022)
Scientific Reports (2019)
Scientific Reports (2019)
Social Network Analysis and Mining (2019)
Scientific Reports (2018)