Abstract
Boltzmann–Gibbs statistical mechanics applies satisfactorily to a plethora of systems. It fails however for complex systems generically involving nonlocal space–time entanglement. Its generalization based on nonadditive q-entropies adequately handles a wide class of such systems. We show here that scale-invariant networks belong to this class. We numerically study a d-dimensional geographically located network with weighted links and exhibit its ‘energy’ distribution per site at its quasi-stationary state. Our results strongly suggest a correspondence between the random geometric problem and a class of thermal problems within the generalised thermostatistics. The Boltzmann–Gibbs exponential factor is generically substituted by its q-generalisation, and is recovered in the \(q=1\) limit when the nonlocal effects fade away. The present connection should cross-fertilise experiments in both research areas.
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Introduction
Boltzmann–Gibbs (BG) statistical mechanics constitutes one of the pillars of contemporary theoretical physics. As such is has uncountable successes for a great variety of physical systems. However, when the system constituents have a generically nonlocal space–time entanglement, this theory does not apply. Such is the case already pointed in 1902 by Gibbs himself, namely when the standard partition function diverges, e.g., gravitation. It is in this context that it was proposed in 19881 the generalisation—hereafter referred to as nonextensive statistical mechanics—of the BG theory based on nonadditive entropies, namely \(S_q = k \frac{1-\sum _i p_i^q}{q-1}\), which recovers \(S_{BG}=-k \sum _{i} p_i \ln p_i\) in the \(q\rightarrow 1\) limit. The composition of two probabilistically independent systems A and B yields straightforwardly \(S_q(A+B)/k= [S_q(A)/k] + [S_q(B)/k] +(1-q) [S_q(A)/k][ S_q(B)/k]\). As we see, the BG entropic additivity is recovered when \(q=1\). The fundamental advantage associated with \(q \ne 1\) is that, for strongly correlated systems, it enables, as illustrated in2, the preservation of the extensivity of the thermodynamic entropy, mandated in all circumstances by the Legendre structure of classical thermodynamics.
In parallel with the above, the study of complex networks has been intensified around the world3,4,5,6,7. Networks can be found everywhere. Society is formed by humans linked through relationships. The Internet is a set of devices communicating with each other. The brain is formed by neurones communicating through synapses. All these completely different systems can be translated onto a simple set of nodes (or sites) and edges (or links) obeying some connection rule, and the tools of network science can be successfully used to study them. Typical applications of this area can be found in classical and quantum internet8,9, medicine10,11, neuroscience12, and sociology13,14. It was thought, during more than a decade, that most of real networks were purely scale-free meaning that the distribution of the number of links in the network follows a power-law distribution. It was recently argued that most real networks are not pure scale-free15, paving the way for new possibilities to describe them.
During the initial years, network science and nonextensive statistical mechanics were seen as completely different areas. But meaningful connections started in 200516,17,18,19,20,21,22. It is nowadays known that the degree distribution of asymptotically scale-free networks at the thermodynamic limit is of the form \(P(k) \propto e_q^{-k/\kappa }\), where the q-exponential function is defined as \(e^{z}_q\equiv [1 + (1-q)z]^{\frac{1}{1-q}}\) (\(e_1^z=e^z\)). This form, more precisely \(p_q(\varepsilon _i) = e_q^{-\beta _q\,\varepsilon _i}/Z_q\), optimizes the entropy \(S_q\) under appropriate canonical constraints, \(\varepsilon _i\) being the site energy and \(\beta _q\) the inverse temperature; the BG weight is recovered at the \(q\rightarrow 1\) limit. This thermostatistical approach has been successfully applied in a wide diversity of areas, such as long-range-interacting Hamiltonian systems23, vortices in type II supercondutors24, cold atoms25, granular matter26, high-energy physics experiments on Earth27 and observations in the outer space28,29, civil engineering30, and for predicting COVID-19 peaks around the world31,32.
In this work, we introduce and study a geographically located d-dimensional network model. One of the main characteristics of this model is the possibility to control the long/short range nature of the interactions between the sites. BG statistics completely fails to describe systems that interact at long-range, and many theories have been proposed to approach this regime. The present model introduces a new property for this class of systems. In addition to the fact that Euclidean distances (\(d_{ij}\)) between the sites are relevant, it also takes into account the weights (\(w_{ij}\)) of the links (see Fig. 1) and associates them to the ‘energy’ (\(\varepsilon _i\)) of each site. Due to that new ingredient, we could compute the energy distribution of the ever growing network. This distribution turns out to have the functional form of the q-generalised BG distribution for nonextensive systems, based on nonadditive entropy. These numerical results strongly suggest a neat correspondence between the random network geometrical problem and a particular thermostatistical problem within the generalised theory.
The model
Our growing d-dimensional network starts with one site at the origin. We then stochastically locate a second site (and then a third, a fourth, and so on up to N) through a probability \(p(r) \propto 1/r^{d+ \alpha _G} \;\;\; (\alpha _G > 0)\), where \(r\ge 1\) is the Euclidean distance from the newly arrived site to the center of mass of the pre-existing cluster; \(\alpha _G\) is the growth parameter and \(d=1,2,3\) is the dimensionality of the system (large \(\alpha _G\) yields geographically concentrated networks).
The site \(i=1\) is then linked to the site \(j=2\). We sample a random number \(w_{ij}\) from a distribution P(w) that will give us the corresponding link weight. Each site will have a total energy \(\varepsilon _i\) that will depend on how many links it has, noted \(k_i\), and the widths \(\{ w_{ij}\}\) of those links. At each time step, the site i only has access to its local energy \(\varepsilon _i\) defined as:
The value of \(\varepsilon _i\) will directly affect the probability of the site i to acquire new links. Indeed, from this step on, the sites \(i =3,4, \ldots\) will be linked to the previous ones with probability
where \(d_{ij}\) is the Euclidean distance between i and j, where j runs over all sites linked to the site i. The attachment parameter \(\alpha _A\) controls the importance of the distance in the preferential attachment rule (2). When \(\alpha _A \gg 1\) the sites tends to connect to close neighbours, whereas \(\alpha _A \simeq 0\) tends to generate distant connections all over the network. Notice that, while the network size increases up to N nodes, the variables \(k_i\) and \(\varepsilon _i\) (number of links and total energy of the i-th node; \(i=1,2,3 \dots ,N\)) also increase in time (see Fig. 1 for a sample of the ever growing network).
If we consider the particular case \(P(w)=\delta (w-1)\), where \(\delta (z)\) denotes the Dirac delta distribution, Eq. (2) becomes \(\Pi _{ij}\propto k_{i}/d^{\,\alpha _A}_{ij} \;\;(\alpha _A \ge 0)\,\), thus recovering the usual preferential attachment rule. Consequently, the present model recovers the one in19,20,21 as a particular instance. Note that, if we additionally consider the particular case \(\alpha _A=0\), we recover the standard Barabási–Albert model with \(\Pi _i \propto k_i\)5,6.
We are considering here the case where w is given by the following stretched-exponential distribution:
which satisfies \(\int _0^\infty dw\,P(w)=1\). As particular cases of Eq. (3) we have: \(\eta = 1\), which corresponds to an exponential distribution, \(\eta = 2\), which corresponds to a half-Gaussian distribution, and \(\eta \rightarrow \infty\), which corresponds to an uniform distribution within \(w \in [0,w_0]\).
Results
Our focus here is to analyse the energy distribution \(p(\varepsilon )\) of the \(N \gg 1\) network. We have in fact analyzed a large amount of typical cases in the space \((d, \alpha _A, \alpha _G, w_0,\eta )\), and have systematically found the same results for \(d=1,2,3\) within the intervals \((\alpha _A/d \in [0,10]; \alpha _G \in [1,10]; w_0 \in [0.5, 10]; \eta \in [0.5,3])\). Similarly to previous works16,19,20,21, \(p(\varepsilon )\) does not depend on \(\alpha _G\); also, it does not depend independently on d and \(\alpha _A\), but only, remarkably, on the ratio \(\alpha _A/d\); \(p(\varepsilon )\) also depends on \(w_0\) and \(\eta\) (see Fig. 2a–d). Because of these features, and without loss of generality, we have once for ever fixed \(\alpha _G = 1\), and \(d=2\). The simulations were done for \(10^3\) realisations of size \(N=10^{5}\), which was verified to be enough for observing the asymptotic distribution \(p(\varepsilon )\) with high precision.
We know that the signature of the Boltzmannian systems is the presence of exponentials and Gaussians distributions. Similarly, the nonextensive systems based on the entropy \(S_q\) can be recognised by the emergence of q-exponentials and q-Gaussians distributions. We have here found that, independent of the choice of \((d, \alpha _A, \alpha _G, w_0,\eta )\), the ‘energy’ distribution \(p(\varepsilon )\) associated with the network is invariably well fitted by the following q-exponential:
where \(p_q(\varepsilon )\) represents the generalisation, within nonextensive statistical mechanics, of the BG energy weight with \(\varepsilon\), \(\beta _q\) and \(Z_q\) playing respectively the roles of energy, inverse temperature and normalisation factor (see Fig. 3). Note that, when \(q\rightarrow 1\), we do recover the standard Boltzmann distribution since \(e_1^{-\beta _1 \varepsilon }\equiv e^{-\beta \varepsilon }\). This result exhibits an interesting emergence of correspondence between a random network geometric problem and a particular case within generalised thermostatistics. This fact definitively reminds the Kasteleyn–Fortuin theorem33, which establishes an important isomorphism between the bond-percolation geometric problem and the \(q_{Potts} \rightarrow 1\) limit of the \(q_{Potts}\)-state Potts ferromagnet.
For all \((d, \alpha _A, \alpha _G, w_0, \eta )\), we found that:
with \(\beta _{q_0} \simeq (-10.81 e^{-1.36\eta } + 6.04)/w_0\) and \(\beta _{q_\infty } \simeq (-4.81 e^{-1.22\eta } + 2.56)/w_0\) . As can be seen, q does not depend on \((\eta ,w_0)\), but only on the scaled variable \(\alpha _A/d\). In contrast, \(\beta _q\) is less universal and depends on all three parameters \((w_0,\eta ,\alpha _A/d)\).
In Fig. 4a,d we show the numerical results for the index q as function of \(\alpha _A/d\). This result is consistent with19,20, where the behaviour of q characterises the existence of three regimes. As can be seen, q is constant and equal to 4/3 in the range \(0 \le \alpha _A/d \le 1\). This interval describes the regime of strong-long-range interactions characterised by the highest value of q. In the interval \(1< \alpha _A/d \lesssim 5\) we have the moderately-long-range interaction regime, characterised by q smaller than 4/3 but still greater than 1. In this regime q displays no abrupt transition from 4/3 to 1 but instead it decreases exponentially with \(\alpha _A/d\) through the function \(e^{1-\alpha _A/d}\)19,20. This behaviour exhibits that the BG regime was not yet reached. In the last regime, \(\alpha _A/d \gtrsim 5\), the Boltzmannian-like regime finally emerges and \(q = 1\). Similar results for q were found in17 for a gas-like network model. In the Fig. 4b,c,e we show similar results for the parameter \(\beta _q\) which equals \(\beta _{q_0}\) in the range \(0\le \alpha _A/d\le 1\) and, then, exponentially decreases with \(\alpha _A/d\); \(\beta _q\) increases with \(\eta\) and decreases with \(w_0\). However, if we plot \((\beta _{q} - \beta _{q_\infty })/(\beta _{q_0} - \beta _{q_\infty })\), all curves collapse as a function of \(\alpha _A/d\). Moreover, we verify in Fig. 4f that the effective ‘temperature’ decreases with increasing q. There is no thermodynamical prescription which would impose that. In30, for instance, both possibilities are in fact observed.
Discussion
All these results strongly suggest that the ‘energy’ distribution \(p(\varepsilon )\) of the network is given by the very same expression which q-generalises the Boltzmann–Gibbs weight when it is the nonadditive entropy \(S_q\) which is optimised. Naturally, since the present study is numerical, we can not exclude very minor corrections due to high-order metric-topological terms. The BG limit is rapidly reached when \(\alpha _A/d\gtrsim 5\). Not less important, q and \(\beta _q\) depend on \(\alpha _A\) and d only through the ratio \(\alpha _A/d\); also, interestingly enough, none of them depends on \(\alpha _G\). The fact that q depends only on \(\alpha _A/d\) means that this ratio uniquely determines the entropic nonadditivity universality class. The quantity \(\beta _q\) also depends on \(\eta\) and \(w_0\). Consistently, \(w_0\), which characterises the width of the stretched-exponential distribution P(w), plays here the same role as T in usual thermal BG problems. This seemingly is the first time that, in a complex network, we identify a parameter which plays the role of an external parameter that we may fix at will, similarly to the manner in which we fix, in BG statistical mechanics, the temperature at which the thermally equilibrated system is placed. In all previous connections with random networks16,17,18,19,20,21,22, \(\beta _q\) (sometimes noted \(1/\kappa _q\)) was univocally related to q. A single value for \(\beta _q\) for a given value of q is analogous to traditional critical points in BG statistics. In our present case, we have, for a fixed value of q, the freedom of making \(\beta _q\) to vary, like T in BG thermal statistics.
The present results strongly support the conjecture of existence of a neat correspondence between geometrical random network (asymptotically) scale-invariant problems and the present specific class of many-body models within nonextensive statistical mechanics, constructed upon nonadditive entropies. This is analogous to the Kasteleyn-Fortuin theorem for the \(q_{Potts}\)-state Potts model, whose \(q_{Potts}\rightarrow 1\) limit rigorously corresponds to the bond percolation problem33, and also to the de Gennes celebrated isomorphism for the n-vector ferromagnetic model, whose \(n\rightarrow 0\) limit precisely corresponds to the self-avoiding random walk34, which constitutes a pillar in polymer physics. It is possible to think of a variety of applications of connections of the present kind, for example the maintaining budgets to be distributed among cities connected within a large regional network of roads. Each city could, for instance, receive a support proportional to the sum of the widths of the roads arriving to it.
Methods
To calculate the relevant properties of our network model in a statistically relevant way, we used 1000 independent realisations within the standard Monte Carlo method to generate different instances of the our network. The network size was set to be \(N=10^5\). All simulations were obtained through independent codes in C. To generate random numbers from the stretched exponential distribution we used the boost library available in https://www.boost.org/. Logarithmic binning was used to generate the histogram of the energies using Python packages of the numpy library.
Data availability
The data and the codes that support all the results within this paper are available from the corresponding authors upon request.
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Acknowledgements
S. B. acknowledges the Serrapilheira Institute (Grant No. Serra-1708-15763), the Brazilian agencies MCTI and MEC. R.M.O., L.R.S. and C.T. acknowledge partial financial support from CAPES, CNPq and Faperj (Brazilian agencies). We also thank the High Performance Computing Center (NPAD/UFRN) for providing computational resources.
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R.M.O. and S.B. performed all the numerical simulations and provided all the results of the manuscript. C.T. proposed the main idea leading to those results. L.R.S., C.T., and S.B. equally contributed to the preparation of the manuscript. All authors conceived and contributed to its conceptual development.
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de Oliveira, R.M., Brito, S., da Silva, L.R. et al. Connecting complex networks to nonadditive entropies. Sci Rep 11, 1130 (2021). https://doi.org/10.1038/s41598-020-80939-1
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DOI: https://doi.org/10.1038/s41598-020-80939-1
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