Abstract
Routing optimization is a relevant problem in many contexts. Solving directly this type of optimization problem is often computationally intractable. Recent studies suggest that one can instead turn this problem into one of solving a dynamical system of equations, which can instead be solved efficiently using numerical methods. This results in enabling the acquisition of optimal network topologies from a variety of routing problems. However, the actual extraction of the solution in terms of a final network topology relies on numerical details which can prevent an accurate investigation of their topological properties. In fact, in this context, theoretical results are fully accessible only to an expert audience and readytouse implementations for nonexperts are rarely available or insufficiently documented. In particular, in this framework, final graph acquisition is a challenging problem inandofitself. Here we introduce a method to extract network topologies from dynamical equations related to routing optimization under various parameters’ settings. Our method is made of three steps: first, it extracts an optimal trajectory by solving a dynamical system, then it preextracts a network, and finally, it filters out potential redundancies. Remarkably, we propose a principled model to address the filtering in the last step, and give a quantitative interpretation in terms of a transportrelated cost function. This principled filtering can be applied to more general problems such as network extraction from images, thus going beyond the scenarios envisioned in the first step. Overall, this novel algorithm allows practitioners to easily extract optimal network topologies by combining basic tools from numerical methods, optimization and network theory. Thus, we provide an alternative to manual graph extraction which allows a grounded extraction from a large variety of optimal topologies. The analysis of these may open up the possibility to gain new insights into the structure and function of optimal networks. We provide an open source implementation of the code online.
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Introduction
Investigating optimal network topologies is a relevant problem in several contexts, with applications varying from transportation networks^{1,2,3,4}, communication systems^{5,6,7}, biology^{8,9} and ecology^{10,11,12}. Depending on the specified objective function and set of constraints of a routing optimization problem^{13}, optimal network topologies can be determined by different processes ranging from energyminimizing treelike structures ensuring steeper descent through a landscape as in river basins^{10} to the opposite scenario of loopy structures that favor robustness to fluctuations and damage as in leaf venation^{12,14}, the retina vascular system^{15,16} or noisecancelling networks^{7}.
In many applications, optimal networks can arise from an underlying process defined on a continuous space rather than a discrete network as in standard combinatorial optimization routing problems^{17,18,19,20}. Optimal routing networks try to move resources by minimizing the transportation cost. This cost may be taken to be a function of the traveled distance, such as in Steiner trees, or proportional to the dissipated energy, such as optimal channel networks or resistance networks. The common denominator of these configurations is that they have a treelike shape, i.e., optimal routing networks are loopless^{1,21}. Recent developments in the mathematical theory of optimal transport^{11,13} have proved that this is indeed the case and that complex fractallike networks arise from branched optimal transport problems^{22}. While the theory starts to consolidate, efficient numerical methods are still in a predevelopment stage, in particular in the case of branched transport, where only a few results are present^{23,24}, reflecting the obstacle that all these problems are NPhard. Recent promising results^{25,26} map a computationally hard optimization problem into finding the longtime behavior of a system of dynamic partial differential equations, the socalled Dynamic MongeKantorovich (DMK) approach, which is instead numerically accessible, computationally efficient, and leads to network shapes that resemble optimal structures^{27}. Working in discretized continuous space, and in many networkbased discretizations such as latticelike networks as well, requires the use of threshold values for the identification of active network edges. This has the main consequence that there might be no obvious final resulting network, an output that would be trivial when starting from an underlying search space formed by predefined selected network structures. For example, the output of a numerically discretized (by, e.g., the Finite Element method) routing optimization problem in a 2D space is a realvalued function on a set of (x, y) points defined on a grid or triangulation, which already has a graph structure. Despite the underlying graph, this grid function contains numerous side features, such as small loops and dangling vertices, that prevent the recognition of a clear optimal network structure. Obtaining this requires a suitable identification of vertices and edges that should contain the optimal network properties embedded in the underlying continuous space. In other words, the output of a routing optimization problem in continuous space carries unstructured information about optimality that is hard to interpret in terms of network properties. Extracting a network topology from this unstructured information would allow, on one hand, better interpretability of the solution and enable the comparison with networks resulting from discrete space. On the other hand, the use of tools from network theory to investigate optimality properties, for instance, to perform clustering or classification tasks based on a set of network features.
One can frame this problem as that of properly compressing the information contained in the “raw” solution of a routing problem in continuous space into an interpretable network structure while preserving the important properties connected to optimality. This is a challenging task, as compression might result in losing important information. The problem is made even more complex because one may not know in advance what are the relevant properties for the problem at hand, a knowledge that could help drive the network extraction procedure. This is the case for any real network, where the intrinsic optimality principle is elusive and can only be speculated about by observing trajectories, an approach adopted for instance when processing images in biological networks^{28,29,30,31}.
Several works have been proposed to tackle domainspecific network extraction. These methods include using segmentation techniques on a set of image pixels to extract a skeleton^{28,29,32} that is then converted into a network; a pipeline combining different segmentation algorithms building from OpenCV^{33}, which is made available with an intuitive graphical interface^{34}; graphbased techniques^{35} that sample junctionpoints from input images; methods that use deep convolutional neural networks^{36} or minimum cost path computations^{37} to extract road networks from images. These are mainly using image processing techniques as the input is an image or photograph, which might not necessarily be related to a routing optimization problem. In this work, we propose a new approach for the extraction of network topologies and build a protocol to address this problem. This can take in input the numerical solution of a routing optimization problem in continuous space as described in^{25,26,27} and then processes it to finally output the corresponding network topology in terms of a weighted adjacency matrix. However, it can also be applied to more general inputs, such as images, which may not necessarily come from the solution of an explicit routing transportation problem. Specifically, our work features a collection of numerical routines and graph algorithms designed to extract network structures that can then be properly analyzed in terms of their topological properties. The extraction pipeline consists in a sequence of three main algorithmic steps: (i) compute the steadystate solutions of the DMK equations (DMKSolver); (ii) extract the optimal network solution of the routing optimization problem (graph preextraction); (iii) filter the network removing redundant structure (graph filtering). While for this work we test and demonstrate our algorithm on routing scenarios coming from DMK, which constitute our main motivation, we remark that only the first step is specific to these, whereas the last two steps are applicable beyond these settings. The graph preextraction step consists of a set of rules aiming at building a network from an input that is not explicitly a topological structure made of nodes and edges. The filtering step is based on a principled mathematical model inspired by that of the first step, which leads to an efficient algorithmic implementation. Our network filter has a nice interpretation in terms of a cost function that interpolates between an operating cost and an infrastructure one, contrarily to common approaches used in image processing for filtering, which often relies on heuristics. Our numerical approach is based on finite elementlike solvers that transform the problem into a finite sequence of linear systems with dimension equal to the number of nodes in the network. Using a careful combination of efficient numerical solvers, the high computational efficiency of our implementation allows addressing large scale problems, out of reach for standard methods of combinatorial optimization. In addition, the algorithmic complexity of our approach is independent of the number of sources and sinks, unlike more standard methods based on Steiner tree solvers^{38,39}.
A successful execution will return a representation of the network in terms of an edgeweighted undirected network. The resulting weights are related to the optimal flow, solution of the routing problem. Once the network is obtained, practitioners can deploy arbitrary available network analysis software^{40,41,42,43} or customwritten scripts to investigate properties of the optimal topologies. For instance, given that our model easily adapts to receive images as input, a promising application is that of extracting optimal network topologies from biological networks, in particular in systems that display a dynamic behavior of selfoptimization, as recently found this being the case for neuronal networks^{44}. Note that our optimal transportbased approach naturally calculates Wassersteintype distances between discrete measures on the network. This can be used, like other geometric approaches in network analysis, to address different networkrelated applications, for example for geometrybased community detection algorithms^{45,46,47}. While our primary goal is to provide a framework and tool to solve the research question of how to extract network topologies resulting from routing optimization problems in continuous space or any other image containing a network structure, we also aim at encouraging nonexpert practitioners to automatically extract networks from such problems or from more general settings beyond that. Thus we make available an opensource algorithmic implementation and executables of this work at https://github.com/Danielaleite/Nextrout.
The routing optimization problem
In this section, we describe the main ideas and establish notation. We start by introducing the dynamical system of equations corresponding to the DMK routing optimization problem as proposed by Facca et al.^{25,26,27} In these works, the authors first generalize the discrete dynamics of the slime mold Physarum Polycephalum (PP) to a continuous domain; then they conjecture that, like its discrete counterpart, its solution tends to an equilibrium point which is the solution of the MongeKantorovich optimal mass transport^{48} as time goes to infinity.
We denote the space where the routing optimization problem is set as \({\varOmega }\in {\mathbb {R}}^{n}\), an open bounded domain that compactly contains \(f(x)=f^+(x)f^(x)\in {\mathbb {R}}\), the forcing function, describing the flow generating sources \(f^+(x)\) and sinks \(f^(x)\). It is assumed that the system is isolated, i.e., no fluxes are entering or exiting the domain from the boundary. This imposes the constraint \(\int _{\varOmega }f(x)dx = 0\) to ensure mass balance. It is assumed that the flow is governed by a transient FickPoiseuille type flux \(q= \mu {\nabla }u\), where \(\mu (t,x),u(t,x)\) are called conductivity or transport density and transport potential, respectively.
The continuous set dynamical MongeKantorovich (DMK) equations are given by:
where \(\nabla =\nabla _{x}\). Equation (1) states the spatial balance of the FickPoiseuille flux and is complemented by noflow Neumann boundary conditions; Eq. (2) enforces the system dynamics in analogy with the discrete PP model and Eq. (3) provides the initial configuration of the system. The parameter \(\beta\) captures different routing transportation mechanisms. A value of \(\beta <1\) enforces optimal solutions to avoid traffic congestion; \(\beta = 1\) is shortest pathlike; while \(\beta > 1\) encourages consolidating the flow so to use a smaller amount of networklike infrastructure, and is related to branched transport^{11,49}. Within a networklike interpretation, qualitatively, \(\mu (x,t)\) describes the capacity of the network edges. With hydraulic interpretation, we can think of the edges as pipes, small cylindrical channels where the mass is passing through, and the capacity is proportional to the size of the pipe diameter. Thus, its initial distribution \(\mu _{0}(x)\) describes how the initial capacities are distributed.
In this work, solving the routing optimization problem consists of finding the steady state solution \((\mu ^*, u^*):{\varOmega }\rightarrow {\mathbb {R}}_{\ge 0}\times {\mathbb {R}}\) of Eq. (1), i.e. \((\mu ^*(x),u^*(x))=\lim _{t\rightarrow +\infty }(\mu (t,x),u(t,x))\). Numerical solution of the above model can be obtained by means of a double discretization in time and space^{25,26,27}. The resulting solver (called from now on DMKSolver) has been shown to be efficient, robust and capable of identifying the typically singular structures that arise from the original problem. In Fig. 1, some visual examples of the numerical \(\mu ^*\) obtained for different values of \(\beta\) are shown. The same authors showed that the DMKSolver is able to emulate the results for the discrete formulation of the PP model proposed by Tero et al.^{50}
Under appropriate regularity assumptions, it can be shown^{26,27} that the equilibrium solution of the above problem \((\mu ^*(x),u^*(x))\) is a minimizer of the following functional:
where \(P(\beta )=(2\beta )/\beta\). In words, this functional is the sum of the total energy dissipated during transport (the first term is the Dirichlet energy corresponding to the solution of the first PDE) plus a nonlinear (subadditive) function of the total capacity of the system at equilibrium. In terms of costs, this functional can be interpreted as the cost of transport, assumed to be proportional to the total dissipated energy, and the cost of building the transport infrastructure, assumed to be a nonlinear function (with power \(2\beta\)) of the total transport capacity of the system.
We exploit the robustness of this numerical solver to extract the solutions of DMK equations corresponding to various routing optimization problems. We here focus on the case \(\beta \ge 1\), where the approximate support of \(\mu ^*\) displays a networklike structure. This is the first step of our extraction pipeline, which we denote as DMKSolver. The numerical solution of these equations does not allow for a straightforward network representation. Indeed, depending on various numerical details related to the spatial discretization and other parameters, one usually obtains a visually welldefined network structure (see Fig. 1) whose rendering as a graph object is however uncertain and nonunique. This in turns can hinder a proper investigation of the topological properties associated to routing optimization problems, motivating the main contribution of our work: the proposal of a graph extraction pipeline to automatically and robustly extract network topologies from the solutions’ output of DMKSolver. We reinforce that our contribution is not limited to this application, but is also able to extract networklike shapes from any kind of image where a color or greyscale thresholds can be used to identify the sought structure.
Our extraction pipeline then proceeds with two main steps: preextraction and graph filtering. The first one tackles the problem of translating a solution from the continuous scenario into a graph structure, while the second one addresses the problem of removing redundant graph structure resulting from the previous step. A pseudocode of the overall pipeline is provided in Algorithm 1. In that pseudocode, meshrelated parameters specify how the mesh for the discretization of space is built. Specifically, we could specify ndiv, the number of divisions in the x axis and nref, the number of refinements, i.e. the number of times each triangle on the grid generated by a specific ndiv is subdivided into four triangles.
Our final goal is to translate the solution pair \((\mu ^*, u^*)\) into a proper network structure using several techniques from graph theory. With these networks at hand, a practitioner is then able to investigate topologies associated with this novel representation of routing optimization solutions.
Graph preliminary extraction
In this section, we expand on the graph preextraction step: extracting a network representation from the numerical solution output of the DMKSolver. This involves a combination of numerical methods for discretizing the space and translating the values of \(\mu ^{*}\), and \(u^{*}\) into edge weights of an auxiliary network, which we denote as \(G=(V,E,W)\), where \(V\) is the set of nodes, \(E\) the set of edges and \(W\) the set of weights.
The DMK solver outputs the solution on a triangulation of the domain \({\varOmega }\) (here also named grid) and denoted as \({\varDelta }_{\varOmega }=\{T_i\}_i\), with \(\cup T_{i}={\varOmega }\). The numerical solution, piecewise constant on each triangle \(T_{i}\), is considered assigned to the triangle barycenter (center of gravity) at position \(\mathbf{b }_{i}=(x_{i},y_{i})\in {\varOmega }\). Note that in this work we focus on a 2D space, but the procedure can be generalized to 3D. This means that the result is a set of pairs \(\left\{ \left( \mu ^{*}(\mathbf{b }_{i}),u^{*}(\mathbf{b }_{i}) \right) \right\} _{i}\). We can track any function of these two quantities. For simplicity, we use \(\mu ^{*}\) (see Fig. 1 for various examples), but one could use \(u^{*}\) or a function of these two. This choice does not affect the procedure, although the resulting network might be different.
We neglect information on the triangles where the solution is smaller than a userspecified threshold \(\delta \in {\mathbb {R}}_{\ge 0}\), in order to work only with the most relevant information. Formally, we only keep the information on \(T_{i}\) such that \(\mu ^{*}(\mathbf{b }_{i})\ge \delta\). We observed empirically that in many cases, several triangles contain a value of \(\mu ^{*}\) that is orders of magnitude smaller than others, see for instance the scale of Fig. 1. Since we want to build a network that connects these barycenters, we remark that this procedure depends on the choice of the threshold \(\delta\): if \(\delta _1<\delta _2\), then \(G({\delta _2}) \subset G({\delta _1})\). On one hand, the smaller \(\delta\), the more likely \(G\) is to be connected, but at the cost of containing many possibly loopforming edges and nodes (the extreme case \(\delta =0\) uses the whole grid to build the final network); on the other hand, the higher \(\delta\), the smaller the final network is (both in terms of the number of nodes and edges). Thus one needs to tune the parameter \(\delta\) such that resulting paths from sources to sinks are connected while avoiding the inclusion of redundant information.
The set of relevant triangles does not correspond to a straightforward meaningful network structure, i.e. a set of nodes and edges connecting neighboring nodes. In fact, we want to remove as much as possible the biases introduced by the underlying triangulation and thus we start by connecting the triangle barycenters. For this, we need rules for defining nodes, edges and weights on the edges. Here, we propose three methods for defining the graph nodes and edges and two functions to assign the weights. The overall graph preextraction routine is given by choosing one of the former and one of the latter, and it can be applied also to more general inputs beyond solutions of the DMKSolver.
Rules for selecting nodes and edges
Selecting \(V\) and \(E\) requires defining the neighborhood \(\sigma (T_{i})\) of a triangle in the original triangulation \({\varDelta }_{{\varOmega }}\) (for i such that \(\mu ^{*}(\mathbf{b }_{i})\ge \delta\)). We consider three different procedures:

(I)
Edgeornode sharing: \(\sigma (T_{i})\) is the set of triangles that either share a grid edge or a grid node with \(T_i\).

(II)
Edgeonly sharing: \(\sigma (T_{i})\) is the set of triangles that share a grid edge with \(T_i\). Note that \(\sigma (T_i)\le 3, \ \ \forall i\).

(III)
Original triangulation: let \(v,\,w,\,s\) be the grid nodes of \(T_i\) ; then add v, w, s to \(V\) and \((v,\,w),\,(w,\,s),\,(s,\,v)\) to \(E\). Note that in this case we make direct use of the graph associated to the triangulation and consider \(\sigma (T_{i})\) as in rule (II).
It is worth mentioning that since the grid \({\varDelta }_{\varOmega }\) is nonuniform and \(\mu ^{*}\) is not constant, we cannot control a priori the degree \(d_{i}\) of a node i in the graph \(G\) generated for a particular threshold \(\delta\). We give examples of networks resulting from these three definitions in Fig. 2 and a pseudocode for them in Algorithm 2.
Rules for selecting weights
The weights \(w_{ij}\) are assigned to edges \(e_{ij}:=(i,j) \in E\) by the function \(w(\mu (\mathbf{b }_{i}),\mu (\mathbf{b }_{j}))\), considering the density defined on the original triangles. We consider two possibilities for this function:

(i)
Average (AVG): \(w_{ij}= \frac{\mu (\mathbf{b }_{i})+\mu (\mathbf{b }_{j})}{2}\) .

(ii)
Effective reweighing (ER): \(w_{ij}= \frac{\mu (\mathbf{b }_{i})}{d_{i}}+\frac{\mu (\mathbf{b }_{j})}{d_{j}}\) .
While using the average as in (i) captures the intuition, it may overestimate the contribution of a triangle when this has more than one neighbor in \(G\) with the risk of calculating a total density larger than the original output of the DMKSolver. To avoid this issue, we consider an effective reweighing as in (ii), where each triangle contribution by the degree \(d_{i}=\sigma _{i}\) of a node \(i\in V\) is reweighted, with \(\sigma _{i}\) the set of neighbors of i. This guarantees the recovery of the density obtained from DMKSolver, since \(\frac{1}{2} \sum _{i,j } w_{ij}= \frac{1}{2}\sum _{i }\left[ \mu (\mathbf{b }_{i}) + \sum _{j \in \sigma _{i}}\frac{\mu (\mathbf{b }_{j})}{d_{j}}\right] =\sum _{i} \mu (\mathbf{b }_{i})\), where in the sum we neglected isolated nodes, i.e. i s.t. \(d_{i}=0\). Note that in the case of choosing the original triangulation for node and edge selection (case (III) above), the ER rule does not apply; in that case, we use AVG, i.e. given an edge e, its weight is the average between its two neighboring triangles.
Graph filtering
The output of the graph extraction step is a network closer to our expectation of obtaining an optimal network topology resulting from a routing optimization problem. However, this network may contain redundant structures like dangling nodes or small irrelevant loops (see Fig. 2). These are not related to any intrinsic property of optimality, but rather are a feature of the discretization procedure resulting from the graph preextraction step. It is thus important to filter the network by removing these redundant parts. However, how to perform this removal in an automated and principled way is not an obvious task. One has to be careful in removing enough structure, while not compromising the core optimality properties of the network. This removal is then a problem inandofitself, we name it graph filtering step. We now proceed by explaining how we tackle it in a principled way and discuss its quantitative interpretation in terms of minimizing a cost function interpolating between an operating and an infrastructural cost.
The DiscreteDMKSolver
Going beyond heuristics and inspired by the problem presented in “The routing optimization problem” section, we consider as a solution for the graph filtering step, the implementation of a second routing optimization algorithm to the network \(G\) output of the preextraction step, i.e. in discrete space. Several choices for this could be drawn, for instance, from routing optimization literature^{51}, but we need to make sure that this second optimization step does not modify any of the intrinsic properties related to optimality resulting from the DMKSolver. We thus propose to use a discrete version of the DMKSolver (discreteDMKSolver). This was proven to be related to the Basis Pursuit (BP) optimization problem^{52}. In fact, BP is related^{53} to the PP dynamical problem in discrete space and the discreteDMKSolver gives a solution to the PP in discrete space^{52}. The discretization results in a reduction of the computational costs for solutions of BP problems, compared to standard combinatorial optimization approaches^{52}. Being an adaptation to discrete settings of our original optimization problem, it is a natural candidate for a graph filtering step, preserving the solution’s properties.
The problem is stated as follows. Consider the signed incidence matrix \({\mathbf {B}} \in {\mathbb {M}}_{N\times M}\) of a weighted graph \(G=(V,E,W)\), with entries \(B_{ie}=\pm 1\) if the edge e has node i as start/end point, 0 otherwise; \(N=V\) and \(M=E\). Denote \(\mathbf {\ell } = \left\{ \ell _{e}\right\} _{e}\) the vector of edge lengths, \({\mathbf {f}}\) a Ndim vector of sourcesink values with entries satisfying \(\sum _{i\in V}f_{i}=0\); this is the discrete analogues of the sourcesink function \({{\mathbf f}(x)}\) introduced in Section “The routing optimization problem”; the functions \(\mu (t)\in {\mathbb {R}}^{M}\) and \(u(t)\in {\mathbb {R}}^{N}\) correspond to the conductivity and potential respectively, similarly to the continuous case, but this time they are vectors with entries \(\mu _{e}(t)\) and \(u_{i}(t)\) defined on edges and nodes respectively. The PP discrete dynamics corresponding to the original routing optimization problem can be written as:
where \(\cdot \) is the absolute value elementwise. Equation (5) corresponds to Kirchoff’s law, Eq. (6) is the discrete dynamics with \(\beta _{d}\) a parameter controlling for different routing optimization mechanisms (analogously to \(\beta\) in Eq. 2); Eq. (7) is the initial condition. The importance of this system stems in having an interesting theoretical correspondence: its equilibrium point corresponds to the minimizer of a cost function analogous to Eq. (4) that, similarly to the continuous case, can be interpreted as global energy functional. This is:
where \(P(\beta )={(2\beta )}/{\beta }\) and \(u(\mu (t))\) is a function implicitly defined as the solution of Eq. (5). The first term corresponds to the energy dissipated during transport, it can be interpreted as the operating costs, whereas the second is the infrastructural cost. The equilibrium point of \(\mu _{e}(t)\) is stationary at the previous energy function, and for \(\beta _{d}=1\) it acts also as the global minimizer due to its convexity. For \(\beta _{d}>1\) the energy is not convex, thus in general the functional will present several local minima towards which the dynamics will be attracted. The case \(\beta _{d}<1\) does not act as a filter because it encourages trajectories to spread through the network, instead of removing edges, and so not interesting to our purposes. Discretization in time of Eq. (6) by the implicit Euler scheme combined with Newton method leads to an efficient numerical solver, see Facca et al.^{52} for more details. The above scheme gives the solution to the BP problem and represents the discreteDMKSolver. Similarly to the graph preextraction step, the filtering is also valid beyond networks related to solutions of the DMKSolver. It applies to more general inputs if defined on a discrete space, for instance, images. Finally, notice that the filter generates a graph with a new set of nodes and edges, both subsets of the corresponding ones in \(G\), result of the preextraction. The weights of the final graph can then be assigned with same rules as in “Rules for selecting nodes and edges”; in addition, one can consider as weights the values of \(\mu ^*_{e}\) resulting from the BP problem (we named this weighing method “BPW”). Alternatively, one can ignore the weights of BP and keep (for the edges remaining after the filter) the weights as in the previous preextraction step (labeled as “IBP”). Analogously to what done on the original triangulation, we discard the edges e for which \(\mu _e<\delta _d\). In our experiments we use as initial density distribution \(\mu _e(0) = w(e), \forall e \in E\), where \(w\) correspond to the weight of the edge \(e\) in the preextracted graph. Figure 3 shows an example of three filtering settings on the same input.
Selecting sources and sinks
The discreteDMKSolver requires in input a set of source and sink nodes (\(S^{+}\) and \(S^{}\)) that identify the support of the forcing vector \(\mathbf{f }\) introduced in “The Discrete DMKSolver”. However, the graph preextraction output \(G\) might contain redundant nodes (or edges) as mentioned before. In principle, among the nodes \(i \in V,\) all of those contained in the support of \(f(x = \mathbf{b}_{i}),\) i.e. contained in the supports of sources and sinks of the original routing optimization problem in Eq. (1), are eligible to be treated as sources or sink in the resulting network. However, several paths connecting source and sink nodes may be redundant and clearly not compatible with an optimal routing network (see Supplementary Fig. S2 for such an example). Therefore, it is important to select “representatives” for sources and sinks, such that the final network is heuristically closer to optimality. Here we propose a criterion to select source and sink nodes from the eligible ones in each of the connected components \(\{C_m\}_m\) of \(G\), using a combination of two network properties. Starting from the complete graph formed by all the nodes characterized by a significant (above the threshold) density, source and sink nodes and rates are defined as follows. A node \(i {\in} S^{+},\) i.e. is a source \(f_{i}>0,\) if either i) is in the convex hull of the set of eligible sources or ii) its betweenness centrality is smaller than a given threshold \(\tau _{BC}\). Similarly for sink nodes in \(S^{}\). This is because, on one side, nodes in the convex hull capture the outer shape structure of the source and sink sets defined in the continuous problem; on the other side, nodes with small values of the betweenness centrality capture the endpoints of \(G\) inside the source and sink sets, analogously to leaves (i.e., degreeone nodes). Note that, due to the high graph connectivity, degree centrality is not appropriate for selecting these ending parts. We present these ideas in more detail in the Supplementary Fig. S2. Once we have identified the sets of source and sink vertices, we need to assign a proper value \( f_{i} \) such that Kirchhoff law is satisfied in each of the different connected components \(C_{m}\). It is reasonable to assume that each connected component is “closed”, i.e. \(\sum _{i \in C_{m}} f_{i}=0 \,, \forall C_{m}\). Denoting with \(S\) the number of elements in a set \(S\) and \(V(C_{m} ) \) the set of nodes in \(C_{m}\), we then distribute the massfluxes uniformly by setting \(f_{i}=\frac{1}{S^{+}\cap V(C_m)}\) for \(i \in S^{+}\), and \(f_{i}=\frac{1}{S^{}\cap V(C_m)}\) for \(i \in S^{}\) sinks (\(f_{i}=0\) otherwise) so that the total original source and sink flux is assigned to the overall source/sink nodes of all \(C_{m}\). Note that this procedure maintains the overall system and each connected component “closed”, as stated above.
Computational complexity
The numerical implementation of our graph extraction algorithm is based on finite elementlike solvers that transform the problem into a finite sequence of linear systems. This implies that we need to run a variable number \(N_{T}\) of iterations in time, each requiring \(N_{N}\) Newton steps. Every Newton step requires the approximate solution of a linear system of dimension N by preconditioner conjugate gradient solver, which has complexity \(O(N\log N)\)^{54}. The time complexity of our graph extraction algorithm is then \(O(N_{T}\, \times N_{N}\, \times N\log N)\). In practice, because of exponential convergence of the time discretization towards equilibrium^{52}, \(N_{T}\) is typically constant approximately \(<10^{2}\), instead \(N_{N}\sim 5\). In the worst cases \(N_{T}\, N_{N}\sim N^{0.3}\). Thus the total complexity is \(O(N\log N)\).
The time complexity of other related approaches such as the ORCbased algorithms is dominated by the computation of the Wasserstein distance, which typically takes \(O(M\,k\times k^{3}\log k)\), where \(k=2M/N\) is the average network degree, when using linear programming and can be further improved using wavelet earthmoverdistance approximation approaches^{55}. While \(M>N\), in sparse networks such as those used in our experiments, \(M\sim N\).
Other approaches that solve similar problems are based on Steiner tree solvers^{39} and have a complexity which depends on the number of sources and sinks, in addition to the system size. Instead, our method complexity does not depend on them, but only on the network size.
Model validation
Our extraction pipeline proceeds by compressing routing information in the raw output of the DMKSolver (although what follows is not restricted to this case) on a lean network structure. This might lead us to lose relevant information in the process. Hence, we need to devise a posteriori estimates that provide quantitative guidance on the “leanness” and information loss of the final network. Here we propose metrics to evaluate the compression performance of the various graph preextraction and filtering protocols. The raw information is made of a set of weights \(w(T_{i})\) representing the values \((\mu ^{*},u^{*})\) on each of the triangles \(T_{i}\in {\varOmega }\). We consider as the truth benchmark the distribution of \(w\), or any other quantity of interest, supported on the subgrid \( \Delta _{\Omega }^{\delta } \subset \Delta _{\Omega } \) formed by all triangles where \(w\) is larger than the threshold value \( \delta \), i.e., \({\varDelta }_{\varOmega }^{\delta }:=\{T_i \in {\varDelta }_{\varOmega }: w(T_i)\ge \delta \}\). We expect that a good compression scheme should preserve both the total amount of the weights from the original solution in \({\varDelta }^{\delta }_{{\varOmega }}\) and the information of where these weights are located inside the domain \({\varOmega }\). Also, we want this compression to be parsimonious, i.e. to store the least amount of information as possible. We test against these two requirements by proposing two metrics that measure: i) an information difference between the raw output of the DMKSolver and the network extracted using our procedure, capturing the information of where the weights are located in space; ii) the amount of information needed to store the network.
Our first proposed metric relies on partitioning \({\varOmega }\) in several subsets and then calculating the difference in the extracted network weights and the uncompressed output, locally within each subset. More precisely, we partition \({\varOmega }\) into \(P\) non intersecting subsets \(C_{\alpha } \subset {\varOmega }\), with \(\alpha =1,\dots ,P\) and \(\cup _{1}^{P}C_{\alpha }={\varOmega }\). For example, we define \(C_\alpha = [x_i,x_{i+1}]\times [y_j,y_{j+1}]\), for \(x_i,x_{i+1},y_j\) and \(y_{j+1},\) consecutive elements of Nregular partitions of [0, 1], and \(P=(N1)^2\). Denote with \(w_{\delta }(T_{i})\) the weight on the triangle \(T_{i}\in {\varDelta }^{\delta }_{{\varOmega }}\), resulting from the DMKSolver (usually a function of \(\mu ^*\) and \(u^*\)). If we denote the local weight of \({\varDelta }_{\varOmega }^{\delta }\) inside \(C_{\alpha }\) as \(w_{\alpha }=\sum _{i: \mathbf{b }_{i}\in C_{\alpha }}w_{\delta }(T_{i})\), then we propose the following evaluation metric:
where \({\mathbb {I}}_\alpha (e)\) is an indicator of whether an edge \(e=(i,j) \in E\) is inside an element \(C_{\alpha }\) of the partition, i.e. \({\mathbb {I}}_\alpha (e)=1,0,1/2\) if both \(\mathbf{b }_{i},\mathbf{b }_{j}\) are in \(C_{\alpha }\), none of them are, or only one of them is, respectively. In words, \({\hat{w}}_{q}(G)\) is a distance between the weights of the network extracted by our procedure and the original weights, output of the DMKSolver, over each of the local subsets \(C_{\alpha }\). This metric penalizes networks that either place largeweight edges where they were not present in the original triangulation, or lowweight ones where they were instead present originally. In this work, we consider the Euclidean distance, i.e. \(q=2\), but other choices are also possible. Note that \({\hat{w}}_{q}(G)\) does not say anything about how much information was required to store the processed network. If we want to encourage parsimonious networks, i.e. networks with few redundant structures, then we should include in the evaluation the monitoring of \(L(G)=\sum _{e\in E} \ell _{e}\), the total path length of the compressed network, where the edge length \(\ell _{e}\) can be specified based on the application. Standard choices are uniform \(\ell _{e}=1,\, \forall e\) or the Euclidean distance between \(\mathbf{b }_{i}\) and \(\mathbf{b }_{j}\). Intuitively, networks with small values of both \({\hat{w}}_{q}(G)\) and \(L(G)\) are both accurate and parsimonious representations of the original DMK solutions defined on the triangulation.
We evaluate numerous graph extraction pipelines in terms of these two metrics on various routing optimization problem settings and parameters. In Fig. 4 we show the main results for a distribution of 170 networks obtained with \(\beta \in \left\{ 1.1,\, 1.2,\, 1.3\right\}\) and \(\beta _{d}=1.1\). Similar results were obtained for other parameter settings. Networks are generated as follows: first, we choose a set of 5 different initial transport densities \(\mu _0\), grouped in parabolalike, deltalike and uniform distributions, and a set of 12 different configurations for sources/sinks (mainly rectangles placed in different positions along the domain, see Supplementary Information for more details). Then, for each of these setups, we run our procedure: (i) first the DMKSolver calculates the solution of the continuous problems; (ii) then we apply the graph preextraction procedure according rules of “Rules for selecting nodes and edges" and weights as in “Rules for selecting weights”; iii) finally, we run the graph filtering step and consider various weight functions, as described in Fig. 4.
We observe that not applying the final filtering step and considering rule I with ER to build the graph (IERNone), the values of \({\hat{w}}_2(G)\) are smaller than other cases. This is expected as by filtering we remove information and thus achieve better performance with this metric when compared to no filtering. However, we pay a price in terms of total relative length as \(L(G)/L_{max}\) is larger for this case. When working with rule II, we notice the appearance of many nonoptimal small disconnected components, and this effect deteriorates if filtering is activated. Corresponding statistics show low values for both \({\hat{w}}_2(G)\) and \(L(G)/L_{max}\). We argue that this is because rule II produces, by construction, fewer redundant objects than rule I in the initial phase. This might have a similar effect as a filter but is done a priori during the preextraction, because rule II produces in this phase a limited number of effective neighbors. However, this comes at a price of higher variability with the sampled networks, as the variance of \({\hat{w}}_2(G)\) is higher than for the other combinations. Among the possibilities with filtering applied, we observe that rule I performs better than rule III, while all the weighting rules give a similar performance in terms of both metrics. Any combination involving rule I plus filtering has a similar performance as rule II in terms of both metrics but with smaller variability. Finally, these combinations perform differently in terms of the number of disconnected components (not shown here), with rule II producing more spurious splittings, as already mentioned. Depending on the application at hand, a practitioner should select one of these combinations based on their properties as discussed in this section. We give an example of a network generated with IERER in Fig. 5.
Application: network analysis of a vein network
We demonstrate our protocol on a biological network of fungi foraging for resources in space. The network structure corresponds to the fungi response to food cues while foraging^{56}. Edges are veins or venules and connect adjacent nodes. This and those of other types of fungi are well known networks typically studied using image segmentation methods^{28,29,30,31}. It is thus interesting to compare results found by these techniques and by our approach, under the conjecture that the underlying dynamic driving the network structure could be the same as the optimality principles guiding our extraction pipeline. In particular, we are interested in analyzing the distribution \(P(\ell )\) of the vein lengths, i.e. the network edges. The benchmark \(P(\ell )\) distribution obtained by Baumgarten and Hauser^{28} using image processing techniques is an exponential of the type \(P(\ell ) = P_{0}\,e^{\gamma \ell }\). Accordingly, as shown in Fig. 6, we find that an exponential fit (with values \(P_0=234.00, \gamma = 36.32\)) well captures the left part of the distribution, i.e. short edges. Differences between fit and observed data can be seen in the rightmost tail of the graph, corresponding to longer path lengths, where the data decay faster than the fit. However, we find that the exponential fit is nevertheless better than other distributions, such as the gamma and lognormal proposed in Dirnberger and Mehlhorn^{57} for the P. Polycephalum. Drawing definite quantitative conclusions is beyond the scope of our work, as this example aims at a qualitative illustration of possible applications that can be addressed with our model. In general, however, it seems not possible to choose a single distribution that well fits both center and tails of the distribution for various datasets of this type^{57}.
To conclude, we demonstrate the flexibility of our graph extraction method on a more general input than the one extracted from DMKSolver. Specifically, we consider as example an image of P. Polycephalum taken from data publicly available in the Slime Mold Graph Repository (SMGR) repository^{58}. We first downsample an image of the SMGR’s KIST Europe data set, using OpenCV (left) and a color scale defined on the pixels as an artificial \(\mu ^*\) function. We build a graph using the graph preextraction and graph filtering steps as shown in Fig. 7. Notice that our protocol in its standard settings with filtering can only generate treelike structures. Therefore, if we want to obtain a network with loops as we did in Fig. 7, we should consider a modification of our routine, which can be done in a fully automatized way, as explained in more details in the Supplementary S4. In short, after the graph preextraction step, where loops are still present, we extract a treelike structure close to the original loopy graph and give this in input to the filtering. We can then add a posteriori edges that connect terminals that were close by in the graph obtained from the preextraction step but removed by the filter, thus recovering loops. In case obtaining loops is not required, our routine can be used with no modifications. Adapting our filtering model to allow for loopy structures in a principled way, analogously to what done in “Graph preliminary extraction", will be subject of future work.
Discussion
We propose a graph extraction method for processing raw solutions of routing optimization problems in continuous space into interpretable network topologies. The goal is to provide a valuable tool to help practitioners bridging the gap between abstract mathematical principles behind optimal transport theory and more interpretable and concrete principles of network theory. While the underlying routing optimization scheme behind the first step of our routine uses recent advances of optimal transport theory, our tool enables automatic graph extraction without requiring expert knowledge. We purposely provide a flexible routine for graph extraction so that it can be easily adapted to serve the specific needs of practitioners from a wider interdisciplinary audience. We thus encourage users to choose the parameters and details of the subroutines to suitably customize the protocol based on the application of interest. To help guiding this choice, we provide several examples here and in the Supplementary Information. We anticipate that this work will find applications beyond that of automating graph extraction from routing optimization problems. We remark that two of the three steps of our protocol apply to inputs that might not necessarily come from solutions of routing optimization. Indeed, the pipeline can be applied to any image setting where an underlying network needs to be extracted. This can have relevant impact in applications involving biological systems like neuronal networks, for which we observe an increasing amount of data from imaging experiments. The advantage of our setting with respect to more conventional machine learning methods is that the final structure extracted with our approach minimizes a clearly defined energy functional, that can be interpreted as the combination of the total dissipated energy during transport and the cost of building the transport infrastructure. We foresee that this minimizing interpretation together with the simplification of the pipeline from abstract modeling to final concrete network outputs will foster crossbreeding between fields as our tool will inform network science with optimal transport principles and viceversa. In addition, we expect to advance the field of network science by promoting the creation of new network databases related to routing optimization problems. For instance, an interesting direction for future work is to extend our optimal transportbased method to address other networkrelated applications such as geometrybased community detection.
Code availability
open source codes and executables are available at https://github.com/Danielaleite/Nextrout.
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Acknowledgements
The authors thank the International Max Planck Research School for Intelligent Systems (IMPRSIS) for supporting Diego Baptista and Daniela Leite.
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All authors contributed to developing the models, analyzing the results and reviewing the manuscript. E.F, M.P. and C.D.B. conceived the experiment(s), D.B. and D.L. conducted the experiments.
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Baptista, D., Leite, D., Facca, E. et al. Network extraction by routing optimization. Sci Rep 10, 20806 (2020). https://doi.org/10.1038/s41598020770644
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DOI: https://doi.org/10.1038/s41598020770644
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