 Software
 Open access
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Multilayer network alignment based on topological assessment via embeddings
BMC Bioinformatics volume 24, Article number: 416 (2023)
Abstract
Background
Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology that allows mapping nodes between two or multiple given networks, by preserving topologically similar regions. For instance, NA can be applied to transfer knowledge from one biological species to another. In this paper, we present DANTEml, a software tool for the Pairwise Global NA (PGNA) of multilayer networks, based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two multilayer networks of interest, to evaluate their topological similarities. The proposed solution can be used via a userfriendly command line interface, also having a builtin guided mode (stepbystep) for defining input parameters.
Results
We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. DANTEml over performed a method that does not consider the distribution of nodes and edges over multiple layers by 1193.62%, and a method for temporal NA by 25.88%; we also performed the statistical assessment, which corroborates the significance of its own node mappings. In addition, we tested the proposed solution by using a real multilayer network in presence of several levels of noise, in accordance with the same outcome pursued for the NA on our dataset of synthetic networks. In this case, the improvement is even more evident: +4008.75% and +111.72%, compared to a method that does not consider the distribution of nodes and edges over multiple layers and a method for temporal NA, respectively.
Conclusions
DANTEml is a software tool for the PGNA of multilayer networks based on topological assessment, that is able to provide effective alignments both on synthetic and real multi layer networks, of which node mappings can be validated statistically. Our experimentation reported a high degree of reliability and effectiveness for the proposed solution.
Background
Network graphs (or simply networks) allow modelling the real world objects in terms of their relationships, by visualizing how the objects (nodes) are interconnected (edges) with each other. To give an example, these allow investigating topological and biological hypotheses based on the interactions existing between biological objects, by applying techniques of representation learning, clustering, or data mining [1, 2].
A multilayer network is an effective choice when several types of entities need to coexist in the same model, and their own connections are modelled. It is modelled a set of nodes, edges, and layers, where the interpretation of the layers depends on the implementation of the model, that may be homogeneous or heterogeneous based on the types of entity represented by the nodes [3]. Furthermore, it supports both intralayer and interlayer edges. The former is fully similar to the concept of edge described for classic networks, the second one instead represents a connection between nodes included in different layers.
Within this context, the relationship between temporal networks and multilayer networks should be mentioned. A temporal network (or timevarying network, or by extension dynamic network) [4] allows modelling a structure composed of the same type of entities, whose topology evolve over time [5, 6]. It can be defined in terms of a multilayer network [7], that includes temporal information on the edges. Therefore, we may consider the temporal networks as a particular case of multilayer networks, where the distribution does not occur in time spans but on layers of interest. Therefore, temporal networks (or timevarying networks) were included among the classes of multilayer networks [8], where each layer encodes the same type of interaction at different time points; briefly, each time point is a layer, representing objects in the same evolutionary timewindow.
This assumption allowed us to extend our own existing solution for the alignment of dynamic networks (i.e., DANTE [9]), to support the alignment of multilayer networks.
Reflecting how described in the context of biological system, the multilayer networks allow properly modelling the interconnected units in a structure able to properly represent the evolutionary and heterogeneous nature of these, by interconnecting even more than one set of biological objects.
Network Alignment (NA) compares networks by finding a node mapping that conserves topologically similar regions from these. Therefore, the alignment of two given networks (i.e., source and target networks) produces a set of aligned node pairs, where a node of the first network is mapped to one of the second one [10]. In the Local NA (LNA), we will look to the matches between local regions, admitting manytomany ones. In the Global NA (GNA), we will look to the best overlapping between the whole topologies of interests, only admitting onetoone matches [11].
NA is a technique used to transfer knowledge from a network to another having a more complex topology. To give a nonexhaustive example, some genes in rat or mouse share functions with the genes in humans. Therefore, NA may be used to transfer this information, to simplify the invivo genotyping, as well as in vitro studies, by assuming that the similarities between the interaction in the networks of two species correspond to similarities in biological processes.
The scope of multilayer networks is still in an early stage, so that the literature does not report solutions specifically designed for the Pairwise GNA (PGNA) of multilayer networks. Nowadays, the only solution supporting the NA of multilayer networks has been proposed by Milano et al. [12] (i.e., MultiLoAl). However, it exploits a LNA heuristic, based on a set of seed nodes and the homology of the given networks.
The existing methodologies for PGNA do not properly process the multilayer networks, having been developed for other network’s models. For completeness, we propose an overview of the wellknown ones, by focusing on those involved in our experimentation.
MAGNA++ [13] is perhaps the most used tool for comparative purposes in the field of PGNA of static networks. It works by applying a genetic approach for maximizing node and edge conservation over successive permutations. Similarly, DynaMAGNA++ [14] performs the PGNA of dynamic networks, based on event durationbased representation. Briefly, it handles a dynamic network as a set of static networks, each of which represents a specific time point. The evolution over time is maintained by evaluating the changes between the topologies of successive time points, by applying an extension of Graphlet Degree Vector (GDV) [15] for dynamic networks. GDV is also used by DynaWAVE for node conservation, otherwise, it performs the alignment via a greedy seedandextend strategy. According to the authors, this different approach is less accurate but faster than DynaMAGNA++.
In this paper, we present DANTEml (DANTE for MultiLayer networks), a novel software tool for the PGNA of multilayer networks, that uses topological assessment to build its own similarity matrix. As discussed, it is based on DANTE (see “Section DANTE: an algorithm for aligning dynamic networks” for detailed information), our existing solution for the PGNA of dynamic networks. Just like its progenitor, DANTEml builds the similarity matrix, on which the alignment function is performed, by analysing the embeddings computed for all nodes.
Our contribution is providing a readytouse tool, purely dedicated to the PGNA of multilayer networks, since the literature only reports methodologies applicable to individual subcategories (e.g., temporal networks, and heterogeneous networks).
Finally, the main features of DANTEml are summarized as follows:

computing the similarities between two multilayer networks of interest, based on node embeddings, in order to reflect their topological similarities;

performing the GNA between pairs of multilayer networks;

providing a userfriendly Command Line Interface, usable also by nonhuman operators for automating data processing, e.g., via scripting or pipelines.
Design and Implementation of DANTEml
In this section, we present the design and implementation of DANTEml, by also focusing on the methods applied for processing, as well as on its use. Before dealing in details, let we firstly introduce how its own progenitor (DANTE) works for aligning pairs of dynamic networks, and how a multilayer network graph may be formally modelled.
DANTE: an algorithm for aligning dynamic networks
Let us denote the source dynamic network by G(V, E1), with \(V = \{v_1,v_2,\ldots ,v_n\}\) (n the number of nodes in G), and the target dynamic network by H(U, E2), with \(U = \{u_1,u_2,\ldots ,u_m\}\) (m the number of nodes in H); we assumed without loss of generality: \({}V{} \le {}U{}\). E1 and E2 are the events (i.e., temporal edges) of G and H, respectively.
DANTE performs the PGNA based on three main steps: (i) evaluating the node features for each dynamic network (i.e., temporal embedding), (ii) constructing the similarity matrix, and (iii) performing the onetoone node mapping between the source and the target.
The temporal embedding is induced by applying the SkipGram (SG) model [16] over the time points, iteratively. In the similarity matrix, the values are obtained by computing the cosine similarity between the pairs of vector embeddings, belonging to a node of the source network and a node of the target one, respectively.
DANTE performs the onetoone node mapping (f) between G and H, by maximizing the objective function (\(\phi\)) between all pairs of nodes. Briefly, it aligns a node of G with a node of H, such that no node of G maps more than one node of H, and vice versa.
The alignment will provide a set of aligned nodes based on best match among all vectors, by handling the collisions via the global maximization of f.
The mapping \(f: V \rightarrow U\) was implemented by adopting an iterative process, so that it produces a set of aligned node pairs (v, f(v)), with \(v \in V\).
Formally, f is computed as follows:
where the objective function \(\phi\) is defined as:
The Graph Model of a Multilayer Network
Formally, a multilayer network graph model \(G_M\) can be described as \(G_{M} = (V_M, E_M)\) [17], where \(V_M\) and \(E_M\) are a set of nodes and edges, respectively.
Referring to \(G_{M}\), let us denote a generic intralayer by \(G_{a}\) and a generic interlayer by \(G_{b}\), consisting of its own set of edges \(E_a\) (i.e., intralayer edges) and \(E_b\) (i.e., interlayer edges), respectively. Elaborating, we may formally describe these as follows:
with \(\alpha\) and \(\beta\) the arrays of elementary layers, and (u, v) a generic pair of nodes. Note that in the proposed model, edges are undirected.
The multilayer networks are given in input to DANTEml in accordance with the described model. The latter has been implemented through a data structure based on an attributed edge list. Formally, we will have a set of tuples (u, v, l), such that u and v are the pair of nodes, and l is the attribute reporting the layer on which this edge insists; each intralayer is also defined by its own identifier.
This approach allows tracing it back to the whole topology by only considering the order of assignment of the identifiers (e.g., increasing integers), in order to reconstruct the multilayer network without loss of information. To give a nonexhaustive example of a multilayer network consisting of three layers, we can assign the identifiers 1, 2, and 3, for the first layer, the second one, and the interlayer, respectively.
In addition, a multilayer network can be defined by using an equivalent flat representation, that allows collapsing both intralayer and interlayer into a network graph model where edges and nodes were typed according to what they represented in the multilayer one; a nonexhaustive toy example consisting of two layers is shown in Fig. 1.
DANTEml
DANTEml performs the alignment by inheriting the definition of f and \(\phi\) from DANTE; we are referring to Eqs. 1 and 2, respectively. What differentiates DANTEml from DANTE mainly concerns the construction of the similarity matrix, to which f is applied. Even in this case, it is constructed based on a set of node embeddings, however, it does not consider a temporal evolution of each node, but the interactions of the latter over independent layers.
In a temporal network, the interlayers represented the edges conserved between one time point and the next one, and could be incorporated into the identity of the node that owned them, as an evolution of its own interactions. Otherwise, in this multilayer context, the interlayers link different entities, therefore, they can be treated as independent edges (i.e., no evolutionary) which has the only particularity of being outside the existence layer of the node that owns it.
In this novel context, a generic node is featured on the whole set of its own interactions (i.e., edges), that may also link different layers. This means that the characterization of a node considers all layers, through the analysis of both the intralayer and interlayer edges, in order to evaluate the relationship of each node with the overall topology of the multilayer network. This issue has required a redesign of the way to evaluate the features of the nodes from a topological point of view, in order to build the similarity matrix that is functional for a given pair of multilayer networks.
We have used the SG model via node2vec [18], for the representational learning on the multilayer network graph models in flat representation, to compute the node embeddings. It consists of a simple neural network based on one hidden layer, firstly designed for word2vec [16] and subsequently extended by node2vec.
Let us denote the number of neurons of the hiddenlayer by N, the node embedding is a matrix with a dimension of \(V \times N\) obtained by the dot product between the input and the hidden layer. The output layer consists of the dot product between each vector in the embedding matrix (V vectors) and its output vector (size equals to N), by producing a weighted matrix with size \(N \times V\). For each iteration, the SG model selects a target node over a rolling window (w), of which the size represents the context location (c) at which the node is predicted. To give an example, the model will evaluate the nodes at \(c1\) and \(c+1\), for \(w=1\).
Briefly, node2vec learns the features of a given node by performing a fixed amount of random walks starting from this one, to explore its context and generate a random sample that can be embedded by using the SG model; the resulting output is a vector representation of the topological properties of the node in the network (i.e., node embedding).
DANTEml computes node embeddings for source and target multilayer networks, independently. These two sets of vector embeddings were correlated by calculating the similarities between all possible pairs, made up of a vector from the first set and one from the second one, respectively. In detail, DANTEml calculates the cosine similarity between a simple mean of the projection weight vectors of the given node in the source network, and the vectors for each node in the target network. The resulting similarities constitute the similarity matrix (S). From a point of view of the data structures used for the implementation of our solution, it consists of nested vectors.
Once the similarity matrix has been calculated, DANTEml applies f to S, for computing a preliminary (pre) onetoone node mapping (or premapping). Based on the latter, an iterative approach based on successive permutations was applied to maximize the Node Correctness (NC).
It is important to underline that the similarity matrix can also be acquired from external sources. The latter case is particularly advantageous for further improving the alignment score, on the basis of similarities between biological objects corroborated by the literature. To give an example, if the similarity between two genes exists, it can be used in the alignment step regardless of the topological location of the two genes in the respective networks or layers. Briefly, in this case, the embeddings are performed on the basis of the given similarity matrix, bypassing the indepth evaluation of the networks through random walks.
Each permutation evolves the premapping by searching alternative pairs within the similarity matrix that, even if with a lower similarity between the embeddings related to the nodes of interest, however these have a greater similarity in terms of global overlapping into its own layer with other candidate nodes.
Let us denote \((u_1,v_1)\) as a generic premapping between the node \(u_1\) and \(v_1\), belonging to the source multilayer network and the target one, respectively. In our approach, the optimization of \(u_1,v_1\) means searching a node \(v_2\), such that the ratio between the similarity of the embeddings and the overlapping of nodes is globally the highest. For evaluating an overlapping, we opted for a simple and wellknown metric in the field of node similarity: the Jaccard coefficient (Jc) [19]. We formally describe Jc in Eq. 5.
with u and v the two nodes of interest, and \(\Gamma (u)\) the set of neighbours of u.
This approach is performed by iteratively applying f to the similarity matrix, for each pair of nodes that do not also have a Jc equal to 1 (i.e., the mappings that are not already overlapped); this allows subsequent refinements. If the similarity between the embeddings already reflects the maximum achievable, the pair does not need to be optimized further and is considered as final. Note that the iterative processing is performed until all pairs of node mapping are maximized, or to a permutation amount defined by the user (default: 50% of the size of the premapping; in our experimentation, this threshold has never been reached, and it seemed reasonable to us to contain the running time in the worst case).
Summarizing, the PGNA between two generic networks produces a resulting onetoone node mapping (see “Section Background”). For multilayer networks, the PGNA he must also evaluate the layered arrangement of the nodes, as well as the existence of the interlayer edges. Figure 2 shows a nonexhaustive toy alignment between two multilayer networks having two layers, each one. DANTEml produces an output in accordance with what has just been specified.
Finally, we also report below the main implementation details.
DANTEml was implemented in Python3, and it consists of three main functions related to (i) node embeddings, (ii) similarity matrix construction, and (iii) alignment, respectively.
The first allows for representational learning of a multilayer network graph model, based on the training of vector embeddings via random walks. It allows mapping a node to an embedding space based on its own topological features. The representational learning was processed by using a wellknown Python package, i.e., Gensim [20], while the creation and manipulation of the network graph models was implemented through NetworkX [21]. Scientific computing (e.g., cosine similarity) and statistics were performed by using Scipy [22].
We have also implemented a userfriendly Command Line Interface (CLI) for human and nonhuman (e.g., script or pipeline), also having a builtin guided mode (stepbystep) for defining input parameters. DANTEml’s screenshot is shown in Fig. 3. Listing 1 briefly reports the cornerstones of using DANTEml through the help of the supported options and parameters.
Results
In this section, we report the results from our experimentation. However, first we will report the details of the datasets, and how these were obtained. Our experimentation focused on nontrivial casestudies consisting of heterogeneous entities.
Datasets
Synthetic Multilayer Networks
Let us denote with n the number of nodes for a layer of interest, and with m the number of edges with which a new node attaches to existing nodes. Furthermore, let p be the probability to add a link to randomly chosen existing nodes between the m new edges defined for the layer of interest, and q be the probability that an edge insisting of a pair of nodes having at least another edge is removed (no node must be isolated) on the same layer. Note that when \(p = q = 0\) this model behaves just like the BarabásiAlbert model [23], a wellknown random scalefree network model. The probabilities p and q can be used to simulate the duplication and divergence mechanisms that are detectable in a real biological network. For our experimentation, we generated 10 multilayer networks, by using the following two sets of arbitrarily parameters for each layer: (i) \([n=100, m=2, p=0.5, q=0.4]\) and (ii) \([n=100, m=2, p=0.3, q=0.7]\).
Furthermore, we based the model on 2 layers, with an amount of interlayer edges equal to 20% of the total interactions.
We also generated a set of four noisy versions, for each initial multilayer network, by removing \(5\%\), \(10\%\), \(15\%\), \(20\%\), and \(25\%\) of randomly selected interactions from the whole set of intralayer and interlayer edges.
Briefly, the network pairs to be aligned consisted of the original multilayer network and all its own noisy versions.
Real Multilayer Network
We start by saying that datasets of real biological networks, downloadable and readytouse, are not available. Therefore, we modelled (adhoc) our own real multilayer network, by joining the following wellknown datasets freely provided by Stanford Biomedical Network Dataset Collection (BioSNAP) [24]: DrugDrug Interaction (DDI) network, DiseaseDisease (DD) network, and DiseaseDrug Association (DDA) network.
DDI and DD were used for modelling the two layers of our real network, while DDA for linking these through a set of interlayer edges. In postprocessing, we improved the quality of the network by cleaning it from zero degree nodes, duplicate edges, and nonintersecting objects. The resulting network consisted of 8392 nodes and 128,200 edges, of which 72,809 were interlayer edges.
Similarly to the approach adopted for synthetic networks, we generated the noisy versions of this network. However, the noise was directly applied to the similarity matrix, by noising \(5\%\), \(10\%\), \(15\%\), \(20\%\), and \(25\%\) of the similarities, both for diseases and drugs.
Experimentation
We report the results from our experimentation. Specifically, we tested DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. The information about the datasets is reported in “Section Datasets”.
For the synthetic networks, the similarity matrices were evaluated by DANTEml, while for the real network was used a similarity matrix, produced by simulating the real biological similarities between the entities that represent its nodes. In the latter case, we expect a better alignment score, despite the much larger size of the multilayer network.
Performance evaluation on synthetic dataset
We aligned a set of synthetic networks with DANTEml, its own progenitor (DANTE), and a wellknown method for the NA of static networks (i.e., MAGNA++), in order to compare its performance with a method optimized for dynamic/temporal networks and one that does not take into account the distribution of nodes and edges over multiple layers, respectively. The dataset was partially adapted both for its own progenitor (DANTE) and MAGNA++, in order to make parsable the networks (not being supported, officially): the former considered the layers as time points and the interlayer edges as temporal edges, the latter ignored the distribution of nodes and edges over multiple layers.
We addressed this outcome by performing 50 alignments with each of the mentioned software tools. It concerned the alignment between 10 synthetic multilayer networks and each of its noisy versions generated at \(5\%\), \(10\%\), \(15\%\), \(20\%\), and \(25\%\). Figure 4 shows the related comparative representation of the alignments produced by each software tool of interest, in terms of alignment score. Furthermore, Table 1 reports the average of these, computed for all pairs from our dataset by DANTEml, DANTE, and MAGNA++, on the different level of noise; the overall mean across all groups is also reported.
We also evaluated the alignments in terms of True Positive Rate (TPR) and False Positive Rate (FPR), for plotting the ROC curves (see Fig. 5), as well as to calculate the related AUC (see Table 2). TPR is the sensitivity, while FPR is \(1specificity\).
Statistical assessment
Foremost, we have proved the impossibility for a classical NA method (i.e., MAGNA++) to be able to produce statistically significant node mappings, between networks that are distributed over multiple layers (i.e., multilayer networks), in order to give more prominence to the fact that this issue is instead well processed by our solution. Tables 3 and 4 report the descriptive analysis and the OneWay ANOVA test [25], respectively, for MAGNA++. ANOVA is a wellknown statistical approach for comparing several independent groups, by analysing the variances between and within these, in order to rank features as well as to classify the performance between groups [26]. Therefore, we considered the alignments at different noisy level, to constitute independent groups among which we could assume to exist a relationship due to degradation of the initial topology due to noise; alternatively, the node mappings may be considered as random output, and these should not be considered as valid alignments.
Similarly, the same approach was applied to DANTEml. We corroborated the nonrandomness of the node mappings computed by the latter. Tables 5 and 6 report the descriptive analysis and the OneWay ANOVA test, respectively, for DANTEml.
In addition, the statistical evaluation related to our solution was further explored by also performing a McNemar’s test on all pairwise alignments of our synthetic dataset, in accordance with Mohammadi et al. [27]. Generally, this approach is also indicated for predictive models. We have built a 2x2 contingency table from the average results produced by DANTEml (Case) in aligning all networks of our dataset (noisy ones included), and the best case (manually generated) that perfectly aligns all nodes (Control); data was normalized between 0 and 100. The contingency table (see Table 7) consisted of the following items (row–column indexes):

0–0 no. of pairs aligned correctly by both Case and Control: 61.

0–1 no. of pairs misaligned by Case, but not by Control: 8.

1–0 no. of pairs aligned correctly by Control, but not by Case: 31.

1–1 no. of pairs misaligned by both Case and Control: 0.
The twotailed p value was 0.0004, calculated with McNemar’s test with the continuity correction.
The p value answered this issue: if there is no association between the node mapping processed by DANTE and the correctly aligned nodes, what is the probability of observing such a large discrepancy (or larger) between the number of the two kinds of discordant pairs? A small p value is evidence that there is an association between the node mapping processed by DANTE and the correctly aligned nodes; by conventional criteria, it can be considered to be statistically significant for a value less than 0.05 (\(p < 0.05\)).
Alignment of real multilayer networks
We evaluated the performance of DANTEml on a casestudy based on real multilayer networks.
We computed the alignments by pairing the network with itself, and by directly noising the given similarity matrix by \(5\%\), \(10\%\), \(15\%\), \(20\%\), and \(25\%\), that DANTEml used for node mapping. This approach allowed us to be able to estimate what the expected alignment score could be: since the noise is directly applied to a network based on real entities (i.e., diseases and drugs), the true node mapping will have to refer to the applied noise, any higher scores are possible as the proposed solution evaluates the network topologies, and it could recover noisy structures. Figure 6 shows the results. The latter also shows the alignments produced by DANTE and MAGNA++ on the same dataset. We calculated an average value of 0.88, 0.42, 0.02 for DANTEml, DANTE, and MAGNA++, respectively; the expected result was 0.88, on average.
Discussion
We evaluated the alignments produced by DANTEml, DANTE, and MAGNA++, via wellknown KPI and statistical methods.
We had also initially chosen DynaWAVE and DynaMAGNA++, in that these are (with MAGNA++) wellknown methods, generally used for comparison. Unfortunately, we were forced to exclude them. Both ones are based on DGDV, whose analysis algorithm has been implemented by using the node identifiers to link successive time points. Our dataset consists of multilayer networks that are made up of different nodes for the various layers, we hypothesized that it was this that caused infinite loops during the experimentation, making it impossible to use these methods, and leading us to exclude them.
According to Chen et al. [28], no accepted criteria are generally accepted in literature for evaluating alignment performance or comparing two methods. It is ambiguous to indicate a criterion as the best or goldstandard, however, the assessment of alignment performance through wellknown Key Performance Indicators (KPIs) is globally accepted [29]. Therefore, we have evaluated the performance of DANTEml based on the following wellknown KPIs: NC, Precision, Recall, F1Score, the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC, or AUROC) [30]. Furthermore, we calculated the Matthews Correlation Coefficient (MCC). It is equivalent to chisquare statistics for a 2x2 contingency table [31]. MCC is a value between − 1 and 1 to be interpreted as follows:

perfect alignment (or true node mapping): +1;

no valid information (or random mapping): 0;

inconsistency between alignment and true node mapping: − 1.
NC evaluates the Precision of the alignment, by showing the ratio of aligned node pairs to the true node mapping. However, it is only defined for GNA, and no metric was specially designed for comparing GNA and LNA methods [32], therefore, therefore, we did not consider it appropriate, to compare DANTEml (global NA) and MultiLoAl (local NA), since the former produces a onetoone node mapping, while the latter a manytomany node mapping.
In our experimentation, we reported NC by using its own unitybased normalisation via feature scaling, as alignment score, while Precision and Recall have been combined in F1Score, based on their harmonic mean. Please note that the only mentioned solution specially designed for the alignment of multilayer networks (i.e., MultiLoAl, see “Section Background”) applies a heuristic for the local alignment, that needs of a set of seed nodes to evaluate a similarity based on their homology, not on direct topological analysis. Therefore, MultiLoAl needs the whole set of node pairs that can be considered as perfect matches between the two networks; this approach simulates the biological similarities, e.g., between genes, or the relationships between genes and proteins.
Briefly, we have constructed the pairs to be aligned by defining a set of initial multilayer networks that was aligned with its own noisy counterparts to increasing noise levels. Therefore, we expected nodes to be coupled with their counterparts (i.e., TP), and that as noise increases, the accuracy of the alignment degrades while FP increase. The latter is a physiological result of the fact that as the noise increases, the noisy counterpart varies its topology in a nonnegligible way and some node may become unrecognizable. In fact, it doesn’t represent a real error, so much so that we used this test to measure to what degree of noise the matches can still be considered optimal for our solution. Based on the above considerations, a True Negative (TN) and False Negative (FN) are a truly nonexistent match and a missing match, respectively.
We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks.
The first test demonstrates the efficiency of DANTEml compared to a method that does not consider the distribution of nodes and edges over multiple layers, as well as the improvements apported in comparison with a method only optimized for a specific category of multilayer networks (i.e., temporal networks). Results shows DANTEml over performed the former by \(1193.62\%\) and the latter by \(25.88\%\), in terms of alignment score evaluated on all dataset (see Mean of Table 1).
According to Nahm et al. [30], the interpretation of AUC shows a good performance for DANTEml and DANTE, while it shows a poor one for MAGNA++ (see Table 2). F1Scores are in accordance with the AUC values, by confirming the goodness. The poor results produced by MAGNA++ were expected, in that it does not (rightly) take into account the distribution of nodes and edges between multiple layers. Likewise, we had also already supposed that DANTE would produce valid results, being in any case capable of managing dynamic networks. However, what we must take into consideration is how DANTEml manages to make a significant contribution, both with respect to cases that are not perfectly specific, and to the traditional ones for simple static networks.
DANTEml showed a strong positive relationship between the computed alignment and the true node mapping, while DANTE a negligible relationship. According to our statistical assessment, MAGNA++ showed an inconsistent result that should be discarded.
The statistical assessment was performed via OneWay ANOVA test, and McNemar’s test, as explained in “Section Results”.
We demonstrated the impossibility for a classical NA method to produce statistically significant alignments; already highlighted by the MCC related to MAGNA++.
In addition, we demonstrated the statistical significance of the alignments produced by DANTEml. In addition, we evidenced that there was an association between the node mapping processed by DANTE and the correctly aligned nodes, by also corroborating the nonrandomness of the node mappings computed by the proposed solution via McNemar’s test.
The results validated the hypothesis above; see Tables 4 and 6 for OneWay ANOVA test, while for McNemar’s test the p value is 0.0001. By conventional criteria, the p values are statistically significant (\(p < 0.05\)).
Finally, our method produced effective alignments, whose quality is statistically correlated to the topological similarity existing between the pairs of given networks.
In the third test (see Fig. 6), we pursued the same outcome of NA of synthetic networks, by aligning a real multilayer network with itself, based on the noised similarity matrix (\(5\%\), \(10\%\), \(15\%\), \(20\%\), and \(25\%\) noise).
We computed the expected alignment score from the true node mapping, to simulate a real case in which an optimal solution exists for the NA of multilayer networks. Note that higher scores are possible, as the proposed solution evaluates the network topologies, and it could recover noisy structures. The use of software tools for other network models (i.e., DANTE, and MAGNA++) are reported for illustrative purposes only. According to results (see Fig. 6), DANTEml shows an improvement of \(+111.72\%\) and \(+4008.75\%\) compared to DANTE and MAGNA++, respectively, while it is perfectly in line with the expected result. We hypothesize that this more marked difference, to the advantage of DANTEml, depended on the size of the network; much larger than synthetic ones, and consequently more distributed between the layers. The clear gap in the results demonstrates the clear advantage between a method that supports a model in which heterogeneous objects are distributed across multiple layers, and one that does not recognize this feature.
The resulting scores showed how the proposed solution is able to maintain a high degree of reliability and effectiveness also for the alignment between large networks built on the basis of real data. As discussed, an initial similarity matrix was used to simulate the real biological similarities between the entities that represent the nodes. The alignment scores were slightly better than the expected result, probably due to the successive permutations that are applied by DANTEml to its own premapping to further maximize the node similarities, globally.
Conclusions
In this paper, we present DANTEml, a software tool for the PGNA of multilayer networks based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two given networks of interest.
Results showed that DANTEml over performed MAGNA++ (a method that does not consider the distribution of nodes and edges over multiple layers) by \(1193.62\%\), and DANTE (its own progenitor for temporal network) by \(25.88\%\).
Furthermore, we performed the statistical assessment of the resulting alignments. By conventional criteria, the p values computed for our solution are statistically significant at \(p < 0.05\); this corroborates the significance of its own node mappings. Briefly, DANTEml provided effective alignments of which node mappings were validated statistically. It has also been tested for aligning large multilayer networks based on real data, by showing a high degree of reliability and effectiveness. In this case, DANTEml showed an improvement of \(+111.72\%\) and \(+4008.75\%\) compared to DANTE and MAGNA++, respectively, as well as it was perfectly in line with the expected result.
Finally, DANTEml allowed aligning both synthetic and real multilayer networks, by proving itself an effective method, of which node mappings can be validated statistically.
Availability and requirements

Project name: DANTEml.

Project home page: https://github.com/pietrocinaglia/danteml (accessed on 05 July 2023).

Operating system(s): Platform independent.

Programming language: Python 3.

Other requirements: https://github.com/pietrocinaglia/danteml/blob/main/requirements.txt (accessed on 05 July 2023).

Licence: the software is provided AS IS under MIT Licence.

Any restrictions to use by nonacademics: none.
Availability of data and materials
Additional details on installation and requirements, as well as example data, are available on the dedicated GitHub repository (https://github.com/pietrocinaglia/danteml, accessed on 05 July 2023).
Abbreviations
 NA:

Network Alignment
 GNA:

Global Network Alignment
 LNA:

Local Network Alignment
 PGNA:

Pairwise Global Network Alignment
 SG:

SkipGram
 Jc:

Jaccard coefficient
 CLI:

Command Line Interface
 KPI:

Key Performance Indicator
 NC:

Node Correctness
 AUROC (or AUC):

Area Under the Receiver Operating Characteristic curve
 TP:

True Positive
 FP:

False Positive
 TN:

True Negative
 FN:

False Negative
 TPR:

True Positive Rate
 FPR:

False Positive Rate
 SD:

Standard Deviation
 SE:

Standard Error
 df:

Degrees of freedom
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This work was funded by the Next Generation EU  Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ’Innovation Ecosystems’, building ’Territorial R &D Leaders’ (Directorial Decree n. 2021/3277)—project Tech4You—Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.
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Conceptualization, P.C.; Data curation, P.C. and M.M.; Investigation, P.C.; Methodology, P.C.; writing—original draft preparation, P.C. and M.C.; writing—review and editing, P.C. and M.C. All authors reviewed the manuscript.
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Cinaglia, P., Milano, M. & Cannataro, M. Multilayer network alignment based on topological assessment via embeddings. BMC Bioinformatics 24, 416 (2023). https://doi.org/10.1186/s12859023055085
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DOI: https://doi.org/10.1186/s12859023055085