May 10, 2017 - arXiv:1705.03743v1 [cond-mat.dis-nn] 10 May 2017. Exactly Solvable Random Graph Ensemble with Extensively Many Short Cycles.
Exactly Solvable Random Graph Ensemble with Extensively Many Short Cycles Fabi´ an Aguirre L´ opez,1, 2 Paolo Barucca,3, 4 Mathilde Fekom,5 and Anthony CC Coolen1, 2
arXiv:1705.03743v1 [cond-mat.dis-nn] 10 May 2017
1
Department of Mathematics, King’s College London, The Strand, London WC2R 2LS, United Kingdom 2 Institute for Mathematical and Molecular Biomedicine, King’s College London, Hodgkin Building, London SE1 1UL, United Kingdom 3 London Institute for Mathematical Sciences, 35a South St, Mayfair, London W1K 2XF, United Kingdom 4 University of Z¨ urich, Department of Banking and Finance, Z¨ urich, ZH, Switzerland 5 UFR de Physique, Universit´e Paris Diderot (Paris 7), 5 Rue Thomas Mann, 75013 Paris, France (Dated: May 11, 2017) We introduce and analyse ensembles of 2-regular random graphs with a tuneable distribution of short cycles. The phenomenology of these graphs depends critically on the scaling of the ensembles’ control parameters relative to the number of nodes. A phase diagram is presented, showing a second order phase transition from a connected to a disconnected phase. We study both the canonical formulation, where the size is large but fixed, and the grand canonical formulation, where the size is sampled from a discrete distribution, and show their equivalence in the thermodynamical limit. We also compute analytically the spectral density, which consists of a discrete set of isolated eigenvalues, representing short cycles, and a continuous part, representing cycles of diverging size.
I.
INTRODUCTION
Models with pairwise interacting elements are ubiquitous in physics and are sufficient to capture the phenomenology of many systems, ranging from condensed matter via biology to the social sciences and informatics. The properties of the network of interactions strongly affects the properties of the system under study, and hence the analysis of networks is central in modern physics. Testing statistically whether a specific network property influences the dynamics of a system requires sampling networks where such property is controllable, and for which the probability measure is known. This is why random graph ensembles, especially maximum entropy ones from which it is possible to sample networks systematically with controlled properties, have gained increasing popularity. They range from degree configuration models [1–6], i.e. where the number of connections or nodes are fixed, to more complicated models where a block structure is given to the nodes in the network, as in stochastic block models [7], or other ensembles where clustering, i.e. the tendency of nodes with common neighbours to be connected, is enhanced [8–15]. However, controlling analytically and numerically second or higher-order properties of networks, i.e. node properties that not only depend on first neighbours, such as the density of cycles, is still a great mathematical and analytical challenge, whose range of applications continues to grow [10, 16–22]. So far, nearly all analytical results obtained for random graph ensembles rely on the assumption of the absence of short cycles, the tree-like approximation, and we have analytical solutions only for random graphs were clustering is absent or too weak (or improbable) to be relevant. One of the first random graph ensembles in literature to include short cycles was [23], where a term depending on the number of 3-cycles of the graph was included as a modification to the well known Erd¨ os-R´enyi model (ER). This was done in order to encourage this connec-
tion transitivity in the graph. However, as was found in simulations [23] and in a more rigorous way in [8, 11], unless the graph is particularly small, this approach does not allow for a tuneable number of triangles. Depending on the values and the scaling of the parameters, the model of [23] either stays in a phase very close to the ER model, with a very slight increase in triangles, or it collapses to a condensed phase, where the complete clique has probability one. In this paper we introduce an exactly solvable ensemble of 2-regular random graphs, with an exponential measure that controls the presence of short cycles up to any finite length. The imposition of 2-regularity removes the possibility of a complete clique forming, and forces the graph instead to be partitioned into a set of disconnected cycles of different lengths. This makes the ensemble analytically solvable and perfectly tuneable. The model displays a second-order transition, from a phase dominated by extensively long cycles, to a phase where only (extensively many) cycles of short lengths are present. In section 2 we introduce and solve the model, in its canonical formulation. In section 3 we describe analytically the phases of the ensemble and the critical hypersurface in the space of parameters; from this result we also compute analytically the spectral density of the ensemble. In section 4 we demonstrate the equivalence of the canonical and grand canonical formulations of the model, and in section 5 we show the agreement of the analytical predictions with numerical experiments. In a final discussion section we summarize the results and delineate the future directions for this research, which are twofold. The first is to relax the 2-regularity constraint of the ensemble, in order to make it more directly comparable to realistic networks. The second is to understand better recent analytical approaches to random graph ensembles that involve constraints on the number of closed paths of all lengths, which is equivalent to constraining random graphs via their spectra [24].
2 II.
DEFINITIONS
We define a random graph ensemble over the set of undirected simple regular graphs of degree 2, which we denote by GN . Any graph in GN is necessarily a set of disjoint cycles. The probability assigned to each graph A ∈ GN is chosen proportional to the exponential of a weighted sum of the number of triangles, squares, pentagons, . . . , K-cycles present in A. We refer to this as biasing with respect of the number of short cycles. Thus ! K X 1 exp p(A) = ℓαℓ nℓ (A) , (1) ZN (α) ℓ=3
Here nℓ (A) denotes the number of length-ℓ cycles, i.e. closed paths of length ℓ without backtracking and without over-counting, and α = (α3 , . . . , αK ) ∈ IRK−2 is a vector of control parameters. Note that isolated nodes (ℓ = 1) and dimers (ℓ = 2) cannot occur due to the degree constraint. The factors ℓ in (1) are included for later convenience. The partition function ZN (α) is given by ! K X X ZN (α) = exp ℓαℓ nℓ (A) . (2) A∈GN
ℓ=3
Expression (1) defines a maximum entropy random graph ensemble with respect to the K − 2 observables nℓ (A), whose ensemble averages are controlled by varying the parameters α. The average fraction of the N nodes that will be found in an ℓ-cycle is given by ℓ hnℓ (A)i. (3) N P where hf (A)i = A p(A)f (A). Following the statistical mechanics route, we define a generating function φN (α): mℓ =
φN (α) = N −1 log[ZN (α)/N !]
(4)
with n = (n3 , . . . , nN ) ∈ INN −2 . This decomposition reflects the fact that, in the particular case of GN , we are fortunate that each graph has to be a collection of cycles, and can therefore be identified fully by a sequence n = (n3 , . . . , nN ) that specifies the number of cycles of each possible length up to N , and a labelling of the nodes. The sum over graphs is then performed by summing over all possible sequences n, keeping track of the multiplicity of each sequence via an associated density of states D(n): D(n) =
ℓ=K+1
III. A.
=
N! 2π
Z
nℓ ≥0
π
dω exp iωN +
−π
ANALYTICAL SOLUTION
+
(8)
From this, in combination with (4), we infer that Z π 1 dω N fN (ω,α) φN (α) = log e N −π 2π
(9)
with fN (ω, α) = iω +
n
2ℓ !
N X e−iωℓ 2ℓ
ℓ=K+1
Summation over graphs
To evaluate the partition function (2) we need to perform a sum over graphs. Such sums are usually not analytically tractable, especially when the ensemble definition involves cycles, as is the case in (1). Here we are able to perform the summation by rewriting it as X PK ZN (α) = D(n)e ℓ=3 ℓαℓ nℓ , (6)
K X e(αℓ −iω)ℓ ℓ=3
(5)
The generator φN (α) is minus the free energy density, apart from a factor N ! which reflects (topologically irrelevant) node label permutations. Including this factor will ensure that the limit φ(α) = limN →∞ φN (α) exists.
(7)
PN Apart from the condition N = ℓ=3 ℓnℓ , this density is proportional to N ! but corrected for over-counting due to the indistinguishability of different length-ℓ cycles, giving a divisor nℓ !, and due to the different ways one can number the nodes in each ℓ-cycle without altering the graph (ℓ cyclic permutations, plus ℓ anti-cyclic permutations), giving a further divisor (2ℓ)nRℓ . Using the integral form of π the Kronecker delta δnm = −π (dω/2π)eiω(n−m) , we can thus write the partition function as ! K X Y N! ℓαℓ nℓ ZN (α) = e QN nℓ n ℓ=3 [(2ℓ) nℓ !] ℓ=3 Z π dω iω(N −PN ℓ=3 ℓnℓ ) e × −π 2π Z K Y X e(αℓ −iω)ℓnℓ N! π = dω eiωN 2π −π (2ℓ)nℓ nℓ ! ℓ=3 nℓ ≥0 N Y X e−iωℓnℓ × (2ℓ)nℓ nℓ !
The main quantities of interest (3) for our graph ensemble (1) can be computed from (4) via mℓ = ∂φN (α)/∂αℓ .
N ! δN,PN ℓnℓ ℓ=3 . δnℓ ,nℓ (A) = QN nℓ n !] [(2ℓ) ℓ ℓ=3 A∈GN ℓ=3 N X Y
K X e(αℓ −iω)ℓ ℓ=3
2ℓN
+
N X e−iωℓ . (10) 2ℓN
ℓ=K+1
The limit N → ∞ of (9) can now be obtained by evaluating the integral over ω in (8) via steepest descent: φ(α) = lim extrω fN (ω, α). N →∞
(11)
The extremum is found by solving ∂f (ω, α)/∂ω = 0.
3 B.
Scaling with N of control parameters
We observe that for finite {αℓ } our model cannot exhibit nonzero cycle densities mℓ in the infinite size limit, since the α-dependent term in (10) vanishes for N → ∞. We are therefore led to redefining the parameters α with a size dependent shift, αℓ = α ˜ℓ +
1 log(N ), ℓ
(12)
K
where α ˜ℓ = O(1). We denote the vector of shifted O(1) ˜ = (˜ control parameters by α α3 , . . . , α ˜ K ), and we define ˜ This implies that for N → ∞ we will φN (α) = ϕN (α). ˜ have mℓ = ∂ϕ(α)/∂ α ˜ ℓ , in which now K N n X X e(α˜ ℓ −iω)ℓ e−iωℓ o ˜ = lim extrω iω + ϕ(α) . + N →∞ 2ℓ 2ℓN ℓ=3
fact that all nodes are typically in cycles of length K or less, so m∞ = 0. A second phase, the connected phase, is characterized by finding a finite fraction of the nodes in longer cycles, so here m∞ > 0. The transition separating the phases is marked by bifurcation of m∞ > 0 solutions. If limN →∞ x < 1, the second term of (17) vanishes for N → ∞, and we immediately obtain m∞ = 0. Hence we are in the disconnected phase, and here the asymptotic observables mℓ are simply found by solving
ℓ=K+1
(13)
m∞ = 0 : 1 =
1=
1 2
e(α˜ ℓ −iω)ℓ +
ℓ=3
1 2N
N X
e−iωℓ ,
(14)
ℓ=K+1
1 (α˜ ℓ −iω)ℓ e 2
(15)
This last identity, in combination with (14), prompts us P to introduce m∞ = 1 − ℓ≤K mℓ ∈ [0, 1], which gives the fraction of the nodes that are not in cycles of length K or less. It is for N → ∞ apparently given by 1 N →∞ 2N
m∞ = lim
N X
e−iωℓ
(16)
ℓ=K+1
It follows from (15), that the physical saddle point ω, after contour deformation, must be purely imaginary. We switch accordingly to the new variable x = e−iω ∈ IR+ 0, in terms of which our equations become: K
1=
1 X ℓ ℓα˜ ℓ 1 xe + 2 2N ℓ=3
mℓ = lim
N →∞
m∞ = lim
N →∞
C.
1 ℓ ℓα˜ ℓ xe 2 N 1 X 2N
N X
1 ℓ ℓα˜ ℓ x e . (20) 2
The condition x < 1 for this solution to exist will be met for large values of {α ˜ ℓ }. Upon reducing the control parameters {α ˜ ℓ }, the value of x will increase, and a transition to the connected phase occurs exactly when x = 1. This happens at the critical manifold in the K−2 dimensional parameter space, defined by validity of K X
eℓα˜ ℓ = 2.
(21)
ℓ=3
and that the asymptotic values of the observables mℓ are subsequently given by mℓ =
mℓ =
ℓ=3
Differentiation of this latter expression reveals that the value ω at the extremum is to be solved from K X
1 X ℓ ℓα˜ ℓ xe , 2
xℓ ,
(17)
ℓ=K+1
(18)
To confirm the equations of the connected phase, we need to investigate how the solution x of (17) scales with N as we approach x = 1. Substituting x = 1 − ξ/N , expanding (17) in N , and taking the limit N → ∞ gives 1 mℓ = eℓα˜ ℓ , 2
K
m∞
1 X ℓα˜ ℓ =1− e , 2
(22)
ℓ=3
and the link between ξ and m∞ is m∞ = (1−e−ξ )/2ξ. It turns out that all cycles of finite length L > K will always have vanishing density for N → ∞. This can be seen simply by replacing K → L in the previous analysis, but with αℓ = 0 for all K < ℓ ≤ L. The newly added control parameters with ℓ > K will give α ˜ ℓ = −ℓ−1 log N , and hence mℓ = limN →∞ 21 xℓ eℓα˜ ℓ = limN →∞ 12 xℓ /N = 0, in both phases. We knew that in the disconnected phase all nodes will typically be in the controlled short cycles of length K or less. We may now conclude that, in the connected phase, those nodes that are not in the controlled short cycles (the fraction m∞ > 0) will typically be found in cycles of diverging length. The ensemble’s Shannon entropy [25] is given by X SN = − p(A) log p(A) A∈GN
xℓ
(19)
ℓ=K+1
Phase phenomenology of the ensemble
We will now demonstrate that the solutions to the coupled equations (17,18) give rise to two phases of our graph ensemble. A disconnected phase is characterized by the
K h X ∂φN (α) i = log N ! + N φN (α) − αℓ ∂αℓ ℓ=3
K X mℓ = N log(N ) 1 − + O(N ) ℓ
(23)
ℓ=3
PK 1 PK 1 Since ℓ=3 (mℓ /ℓ) ≤ 3 ℓ=3 mℓ ≤ 3 , the leading order will for large N always scale as N log(N ), and be bounded according to SN ≥ 23 N log(N ) + O(N ), but
4 with a reduced prefactor if we increase the fraction of nodes in short cycles. The lower bound is achieved in the disconnected phase, when m3 = 1 and mℓ>3 = 0. D.
Spectral densities of adjacency matrices
A graph can be represented uniquely by its adjacency matrix {Aij }, where Aij ∈ {0, 1}, and Aij = 1 if and only if there is a link from j to i. The set GN contains only simple nondirected graphs, so our adjacency matrices are symmetric and with zero diagonal elements. The eigenvalue density of the adjacency matrix of a graph A, N 1 X δ[µ − µi (A)], ̺(µ|A) = N i=1
A∈GN
The adjacency matrix of a graph that consists of a single cycle of length ℓ has the Toeplitz form, and is therefore diagonalized trivially, leading to the density ̺ℓ (µ) =
ℓ−1 1X δ µ − 2 cos(2πr/ℓ) . ℓ r=0
(26)
If the cycle length ℓ diverges, this density becomes continuous (in a distributional sense), see e.g. [26], ̺∞ (µ) = lim ̺ℓ (µ) = ℓ→∞
1 θ(2 − |µ|) p . π 4 − µ2
(27)
Within the canonical approach one finds that, if N is sufficiently large, graphs generated randomly from (1) will all display the same values of the main intensive quantities, such as the fraction of ℓ-cycles (modulo finite size fluctuations). We expect a similar claim to hold if we sample randomly both the graphs and the number N of nodes, i.e. if we work with grand canonical graph ensembles. The grand partition function of our ensemble with weights wN = e−µN /N ! (where µ > 0) is given by Q(α) =
ℓ−1 N 2πr X 1 X ) nℓ (A) δ µ − 2 cos N ℓ r=0
=
ℓ=3
N
̺ℓ (µ).
K X e−µℓ ℓ=1
̺(µ) =
K X ℓ=3
mℓ ̺ℓ (µ) + m∞ ̺∞ (µ).
2ℓ
−
K X e(αℓ −µ)ℓ
2ℓ
ℓ=3
hN i =
+
1 log(1−e−µ ). (32) 2
=
K
K
ℓ=3
ℓ=1
1 X (αℓ −µ)ℓ 1 X −µℓ 1 e−µ + e − e 2 1−e−µ 2 2 −µ(K+1)
(29)
(30)
Its partial derivatives with respect to µ and α yield the average system size, via hN i = ∂Ω(α)/∂µ, and the average number of length-ℓ cycles (for ℓ = 3, . . . , K), via hnℓ (A)i = −ℓ−1 ∂Ω(α)/∂αℓ . We thereby find that
(28)
Upon averaging over the ensemble, using (3) and our earlier observation that for N → ∞ the fraction of nodes in cycles of finite length L > K vanishes, we immediately obtain the asymptotic ensemble-averaged spectrum corresponding to (1), expressed in terms of (26) and (27):
wN ZN (α),
with ZN (α) defined in (2). The divisor N ! in wN will simplify our calculation, without losing the benefits of the thermodynamic limit (since we will find that for µ → 0 the expected system size still diverges). Direct calculation of Q(α) now circumvents the integration over ω: ! ∞ K XY e−µℓnℓ Y ℓαℓ nℓ Q(α) = e (2ℓ)nℓ nℓ ! n ℓ=3 ℓ=3 K Y X e−µℓn Y X e(αℓ −µ)ℓn = (2ℓ)n n! (2ℓ)n n! n≥0 ℓ>K ℓ=3 n≥0 ! K X e−µℓ X e(αℓ −µ)ℓ + = exp 2ℓ 2ℓ ℓ>K ℓ=3 ! K K X X e(αℓ −µ)ℓ e−µℓ 1 −µ = exp − log(1−e ) − 2ℓ 2 2ℓ ℓ=3 ℓ=1 (31) P where we used ℓ>0 xℓ /ℓ = − log(1−x). From Q(α) we obtain, in turn, the grand potential Ω(α) = − log Q(α):
ℓ=3
N X ℓnℓ (A)
∞ X
N =1
Ω(α) =
The set of eigenvalues for each A ∈ GN will just be the union of all the sets of eigenvalues of the disjoint cycles of which it is composed, taking multiplicities into account: ̺(µ|A) =
GRAND CANONICAL APPROACH
(24)
contains valuable information on the statistics of cycles in the graph. Here the sum runs over the set of (real) eigenvalues {µi (A)}i=1,...,N of A, taking into account multiplicities. For instance, the number of closed paths in A R is proportional to dµ ̺(µ|A)µℓ . Our main quantity of interest will be the expected density, averaged over the ensemble probabilities (1), in the infinite size limit, X (25) ̺(µ) = lim p(A)̺(µ|A). N →∞
IV.
K 1X
1e + 2 1−e−µ 2
e(αℓ −µ)ℓ
(33)
ℓ=3
and hnℓ (A)i =
1 (αℓ −µ)ℓ e 2ℓ
(34)
Clearly, hN i diverges for µ → 0, which gives our thermodynamic limit. In this limit we can then work out for
5 1.0
1.0
0.8
m3
0.8
0.6
m3
0.6
0.4 0.4
0.2
0.0
0.2 0e+00
2e+04
4e+04
6e+04
8e+04
1e+05
−0.2
0.0
0.2
FIG. 1. Examples of the evolution of the fraction m3 of nodes in triangles, measured during MCMC simulations, for K = 3. Time is defined as the number of accepted edge swap moves per link. The bottom two curves correspond to the connected phase of the ensemble, equilibrating to the values m3 = 0.125 for α ˜ 3 = 13 log(0.25), and to m3 = 0.45 for α ˜ 3 = 13 log(0.9). The top curve corresponds to the disconnected phase, here the MCMC process is equilibrating to the value m3 = 1.
ℓ ∈ {3, . . . , K} the ratios
µ↓0
ℓhnℓ i = lim µ↓0 hN i
e−µ(K+1) 1−e−µ
e(αℓ −µ)ℓ = 0 (35) PK + ℓ′ =3 e(αℓ′ −µ)ℓ′
Similar to the canonical case, any µ-independent α will asymptotically always yield a vanishing fraction of nodes in cycles of length ℓ ≤ K. Again a re-parametrization is required to obtain a non-trivial thermodynamic limit, which is here αℓ = α ˜ℓ + ℓ−1 loghN i. Upon following this prescription, we then reproduce the canonical result 1 ℓhnℓ i = e(α˜ ℓ −µ)ℓ , hN i 2
(36)
and our expression (33) for hN i now becomes hN i =
e−µ(K+1) . PK (1−e−µ ) 2 − ℓ=3 e(α˜ ℓ −µ)ℓ
0.6
0.8
FIG. 2. Values of m3 shown versus α ˜ 3 for ensembles with K = 3. Numerical results, measured upon equilibration of the MCMC processes, are shown as black dots with error bars for N = 1000, and as squares for N = 5000 (error bars for N = 5000 are not shown; their sizes are similar to or smaller than the squares). The solid line is the prediction of (38).
V.
lim
0.4
α ˜3
t
(37)
The re-parametrization of α now depends on α itself, via hN i, and has to be consistent with a nonnegative value P (α ˜ ℓ −µ)ℓ for (37), i.e. with 21 K ≤ 1. Expression (36) ℓ=3 e PK gives us the physical interpretation ℓ=3 ℓhnℓ i/hN i ≤ 1. PK In the limit µ ↓ 0 the condition becomes ℓ=3 eℓα˜ ℓ ≤ 2. PK In the case of inequality we have ℓ=3 ℓhnℓ i/hN i < 1, so we are in the connected phase. The case of equality reproduces our earlier phase transition condition (21) and we enter the disconnected phase; here the thermodynamic limit is reached already for nonzero µ, and we can again recover our canonical equations, with exp(−µ) now playing the role of the canonical order parameter x.
NUMERICAL SIMULATIONS
Calculating ZN (A) by numerical enumeration for nontrivial values of N is not a realistic option, since the size of the set GN grows super-exponentially with N . Instead, to test our theoretical predictions we have sampled graphs from the ensemble (1) using the Markov Chain Monte Carlo (MCMC) method described in e.g. [27] or [6]. Starting from an arbitrary 2-regular N -node graph, this stochastic process is based on executing repeated (degree-preserving) edge swap moves with appropriate nontrivial move acceptance probabilities, constructed such that the Markov chain’s equilibrium distribution is the target measure (1). In each simulation experiment, the MCMC process was first run for 105 to 106 accepted moves per link, and equilibration was confirmed by measuring the Hamming distance between the instantaneous and the initial state. After this randomization stage, the instantaneous state A arrived at by the chain was defined to be our graph sample. We have limited our simulations to ensembles with K = 3 and K = 4. The degree of equilibration achieved by the MCMC during a run of 105 accepted moves per link is illustrated in Figure 1, where we show typical evolution curves of the order parameter m3 during the stochastic process. For K = 3 we have just one control parameter α ˜3, and the order parameter is the fraction m3 of nodes in triangles. The theory claims that, for large N , the graphs from our ensemble will be collections of triangles and large rings. The key equations (20,21,22) reduce to the following predictions, with α ˜ c = 13 log(2) ≈ 0.23105 . . . : α ˜3 < α ˜c :
m3 = 21 e3α˜ 3 ,
connected phase
α ˜3 > α ˜c :
m3 = 1,
disconnected phase
(38)
Numerical simulations with sizes N = 1000 and N = 5000 show excellent agreement with these predictions, as
6 1.0
0.4
Disconnected 0.8
0.2
α ˜4
0.0
−0.2
−0.4
m4
* * Connected
−0.6
−0.8
−0.6
−0.4
0.6
0.4
0.2
*
0.0 −0.2
0.0
0.2
0.4
α ˜3
**
0.0
* 0.2
0.4
0.6
0.8
1.0
m3
FIG. 3. Left panel: the plane of control parameters for K = 4. The solid black line is the critical line e3α˜ 3 + e4α˜ 4 = 2 (here m∞ = 0). The dashed lines correspond to parameter combinations with constant m∞ , taking the values m∞ ∈ {0.75, 0.5, 0.25}, from bottom to top. The markers represent parameter combinations chosen for MCMC simulations. Right panel: the fractions (m3 , m4 ) associated with the control parameter combinations in the left panel. Here the markers represent the simulation results, which are indeed found on the respective lines predicted by the theory. Note that the theory predicts that all parameter combinations in the disconnected phase e3α˜ 3 + e4α˜ 4 ≥ 2, should be mapped to the line m1 + m2 = 1 in the right panel. Error bars were omitted, as they are as big as or smaller than the markers.
shown in Figure 2, both in terms of the values of m3 and in terms of the location of the transition. For K = 4 we have two control parameters, α ˜ 3 and α ˜4, and the theory claims that for large N the graphs from our ensemble will now be collections of triangles, squares and large rings. Here the key equations (20,21,22) predict that the transition line in parameter space is given by e3α˜ 3 + e4α˜ 4 = 2, and that the fractions m3 and m4 of nodes found in triangles and squares, respectively, are solved (together with the auxiliary order parameter x, in the disconnected phase) from: e3α˜ 3 + e4α˜ 4 < 2 :
m3 + m4 < 1,
connected phase
m3 = 21 e3α˜ 3 , m4 = 12 e4α˜ 4 e3α˜ 3 + e4α˜ 4 > 2 :
m3 + m4 = 1,
(39) disconnected phase
m3 = 21 x3 e3α˜ 3 , m4 = 12 x4 e4α˜ 4 Figure 3 (left panel) shows the resulting predicted phase diagram in the (˜ α3 , α ˜ 4 ) plane. The mapping (˜ α3 , α ˜ 4 ) 7→ (m3 , m4 ) will map the lower region of the phase diagram (the connected phase) to the interior of the triangle m3 + m4 < 1 in the right panel of Figure 3. The upper region of the phase diagram on the left (the disconnected phase), including the critical line, will be mapped to the line m3 + m4 = 1 in the right panel. To test also these predictions against numerical simulations, we have chosen multiple points (˜ α3 , α ˜ 4 ) in both regions of the phase diagram, grouped such that the predicted values of m∞ = 1−m3 −m4 were always in the set {0.25, 0.5, 0.75}. The prediction would therefore be that in the (m3 , m4 ) plane these groups of points should be found on the lines m3 + m4 = 1 − m∞ . Upon measuring the fractions m3 and m4 via MCMC in the corresponding graph ensembles, these predictions are once more validated convincingly. See Figure 3.
Graphs sampled from our ensemble with K = 4 do indeed typically consist of controlled numbers of triangles and squares, and a long ring. Figure 4 shows examples of such graphs, obtained via MCMC, together with the eigenvalue spectra of their adjacency matrices (obtained by numerical diagonalization). Also the observed spectra agree with the corresponding theoretical predictions (29).
VI.
CONCLUSIONS
In this paper we presented an analytical solution for an exponential random graph ensemble with a controllable density of short cycles. Whereas one would normally not expect such non-treelike graph ensembles to be solvable, here this is possible as a consequence of imposing a local degree constraint of strict 2-regularity. We found a second order phase transition, which separates a connected phase with large and small cycles from a disconnected phase where the graphs are typically formed only of extensively many short cycles. The short cycles appear in controlled proportions, for which we found analytical expressions in terms of the ensemble’s parameters. We also derived an analytical expression for the critical submanifold in the phase diagram, and for the expected eigenvalue spectrum of the graphs’ adjacency matrices. We analysed both the canonical and the grand canonical formulation of the ensemble. The canonical version was solved via steepest descent integration. In the grand canonical version one avoids steepest descent integration, but (as always) the chemical potential takes over the role of the steepest descent integration variable of the canonical version. In the thermodynamic limit, the canonical and grand canonical routes result in identical equations. These equations are found to give highly accurate predictions already for modest graph sizes, such as N = 1000,
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14
ρ(µ)
1.2
1.2
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10
0.8
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8
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0.4
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4
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−1
0
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0 −2
−1
µ
0
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1
2
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0
1
2
µ
FIG 4 Top row typ ca graphs samp ed numer ca y v a MCMC rom the canon ca ensemb e (1) o 2-regu ar nond rected s mp e graphs or N = 1000 Le t (m3 m4 ) = (0 0 0 06) and m∞ = 0 94 M dd e (m3 m4 ) = (0 25 0 56) and m∞ = 0 19 R ght (m3 m4 ) = (0 39 0 61) and m∞ = 0 The bottom row shows the e genva ue spectra o the correspond ng three ad acency matr ces computed by d rect numer ca d agona zat on The ocat ons o the peaks are seen to agree w th the theoret ca pred ct ons o (29) Note the d fferent sca e n the th rd spectrum graph to emphas ze the we ghts o the δ-peaks
as we confirmed n numer ca s mu at ons The parameter K represents the argest cyc e ength that s contro ed n our mode For K = 3 one contro s on y the number of tr ang es and our ensemb e becomes s m ar to that of Strauss 8 11 23 w th average degree two The rema n ng d fference s that n the Strauss mode the average degree s mposed mp c t y v a an overa soft constra nt wh e n the present mode a degree va ues are mposed as oca hard constra nts Due to th s d fference the degenerat on of the Strauss mode to a phase where the comp ete c que has probab ty one (so the number of tr ang es can no onger be tuned) s avo ded n the present ensemb e The comp ete c que s s mp y no onger an a owed configurat on and hence the number of tr ang es becomes fu y tuneab e f the mode parameters sca e appropr ate y w th the system s ze Our mode defin t on cou d obv ous y be genera zed to nc ude q-regu ar graphs w th q > 2 Numer ca exp orat ons for 3-regu ar vers ons w th K = 4 have been reported n 6 and show phenomeno ogy s m ar to that found here for q = 2 In part cu ar one aga n observes a d sconnected phase for arge va ues of α3 and α4 However ana yt ca so ut on for q > 2 a ong the nes fo owed here for q = 2 wou d not seem feas b e We cou d a so comb ne our present mode w th the Erd¨ os-R`eny ensemb e to produce connected random graphs w th a vary ng
number of short cyc es Aga n wh e the phenomeno ogy of such var at ons cou d be exp ored v a s mu at ons t s not c ear how one wou d be ab e to obta n ana yt ca so ut ons w thout the benefit of oca y tree- ke topo ogy In our v ew the ma n mer t of the present mode s that ts ana yt ca so ut on he ps us understand more comp cated oopy graph ensemb es It can serve as a benchmark mode aga nst wh ch more genera so ut on strateg es for non-tree ke random graphs can be tested such as 24 wh ch dea s w th spectra y constra ned max mum entropy graph ensemb es The moments of a graph s spectra dens ty are re ated to ts numbers of cyc es v a the traces of powers of the adjacency matr x In fact the present mode s a spec a case of the fam y of ensemb es stud ed n 24 from wh ch t can be obta ned by choos ng 2-regu ar degrees and an appropr ate po ynom a funct ona Lagrange parameter The ana yt ca and numer ca resu ts of th s paper suggest that to obta n phase trans t ons the funct ona Lagrange parameters n spectra y constra ned max mum entropy graph ensemb es 24 may need to have a spec fic sca ng w th the system s ze Acknowledgement FAL gratefu y acknow edges financ a support through a scho arsh p from Conacyt (Mex co)
8
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