 Research
 Open Access
 Published:
Fair evaluation of global network aligners
Algorithms for Molecular Biology volume 10, Article number: 19 (2015)
Abstract
Background
Analogous to genomic sequence alignment, biological network alignment identifies conserved regions between networks of different species. Then, function can be transferred from well to poorlyannotated species between aligned network regions. Network alignment typically encompasses two algorithmic components: node cost function (NCF), which measures similarities between nodes in different networks, and alignment strategy (AS), which uses these similarities to rapidly identify highscoring alignments. Different methods use both different NCFs and different ASs. Thus, it is unclear whether the superiority of a method comes from its NCF, its AS, or both. We already showed on stateoftheart methods, MIGRAAL and IsoRankN, that combining NCF of one method and AS of another method can give a new superior method. Here, we evaluate MIGRAAL against a newer approach, GHOST, by mixingandmatching the methods’ NCFs and ASs to potentially further improve alignment quality. While doing so, we approach important questions that have not been asked systematically thus far. First, we ask how much of the NCF information should come from protein sequence data compared to network topology data. Existing methods determine this parameter moreless arbitrarily, which could affect alignment quality. Second, when topological information is used in NCF, we ask how large the size of the neighborhoods of the compared nodes should be. Existing methods assume that the larger the neighborhood size, the better.
Results
Our findings are as follows. MIGRAAL’s NCF is superior to GHOST’s NCF, while the performance of the methods’ ASs is datadependent. Thus, for data on which GHOST’s AS is superior to MIGRAAL’s AS, the combination of MIGRAAL’s NCF and GHOST’s AS represents a new superior method. Also, which amount of sequence information is used within NCF does not affect alignment quality, while the inclusion of topological information is crucial for producing good alignments. Finally, larger neighborhood sizes are preferred, but often, it is the second largest size that is superior. Using this size instead of the largest one would decrease computational complexity.
Conclusion
Taken together, our results represent general recommendations for a fair evaluation of network alignment methods and in particular of twostage NCFAS approaches.
Background
Motivation and related work
Analogous to sequence alignment, which finds regions of similarity that are a likely consequence of functional or evolutionary relationships between the sequences, network (or graph) alignment finds regions of topological and functional similarity between networks of different species [1]. Then, functional (e.g., agingrelated [2–4]) knowledge can be transferred between species across conserved (aligned) network regions. Thus, just as sequence alignment, network alignment can be used for establishing from biological network data orthologous relationships between different proteins or phylogenetic relationships between different species [5–7]. Also, it can be applied to research problems in other domains, such as semantically matching entities in different ontologies [8], or comparing online social networks with impacts on user privacy [9].
Network alignment can be performed locally and globally. Local network alignment (LNA) aims to optimize similarity between local regions of different networks [10–19]. As such, LNA often leads to manytomany node mapping between different networks. However, LNA is generally unable to find large conserved subgraphs. Thus, methods for global network alignment (GNA) have been proposed, which aim to optimize global similarity between different networks and can thus find large conserved subgraphs [2, 3, 5–7, 9, 20–31]. Unlike LNA, GNA typically results in onetoone node mapping between different networks (though some exceptions exist that result in onetomany or manytomany node mapping [24, 32]). In this study, we focus on onetoone GNA due to its recent popularity [2, 3, 31], but all concepts and ideas can also be applied to onetomany or manyto many GNA, as well as to LNA.
More formally, we define GNA as a onetoone mapping between nodes of two networks that aligns the networks well with respect to a desired topological or functional criterion. GNA is a computationally hard problem to solve due to the underlying subgraph isomorphism problem [33]. This is an NPcomplete problem that asks whether a network exists as an exact subgraph of a larger network. GNA is a more general problem which aims to fit well two networks when one network is not necessarily an exact subgraph of another network. Since GNA is computationally hard, heuristic methods need to be sought. Many (though not all) GNA heuristic algorithms typically achieve an alignment via two algorithmic components: node cost function (NCF) and alignment strategy (AS) [5–7, 25, 26, 30, 34–36]. NCF captures pairwise costs (or equivalently, similarities) of aligning nodes in different networks, and AS uses these costs to identify a goodquality alignment out of all possible alignments with respect to some topological or biological alignment quality measure [2, 3, 5–7, 20, 24–26, 34].
Different existing twostep GNA methods use both different NCFs and ASs, so it is unclear whether the superiority of a method comes from its NCF, AS, or both. For this reason, in our recent study [2, 3], we combined NCFs and ASs of MIGRAAL [7] and IsoRankN [24], two stateoftheart methods at the time, as a proof of concept that it is important to fairly evaluate the contribution of each component to alignment quality. In the process, we showed that NCF of MIGRAAL is superior to that of IsoRankN, and importantly, we proposed the combination of MIGRAAL’s NCF and IsoRankN’s AS as a new superior method for multiple GNA, i.e., for GNA of more than two networks at a time [2, 3].
In the meanwhile, a new stateoftheart method has appeared, called GHOST [25]. When recently tested against many other both previous and newer GNA methods, GHOST was described as still “an excellent performer” [31]. Thus, in this study, we aim to understand whether it is GHOST’s NCF or AS (or both) that leads to its good performance, as well as to explore the possibility of further increasing GHOST’s performance by replacing its current NCF with a different, potentially superior NCF. For these reasons, we fairly evaluate MIGRAAL against GHOST by mixing and matching their NCFs and ASs. We use MIGRAAL in this study because we already demonstrated the superiority of its NCF, as discussed above [2, 3]. At the same time, we ask several additional important questions regarding the choice of appropriate GNA parameters, which have surprisingly been neglected thus far.
We note that some of the existing onetoone GNA methods do not belong to this twostage NCFAS method category, and clearly, our study might not directly be applicable to such approaches. However, many of the existing onetoone GNA methods do belong to the twostage category, such as two versions of IsoRank [20, 22], GRAAL [5], HGRAAL [6], MIGRAAL [7], and GHOST [25]. It is very likely that many new methods will build on top of these wellestablished stateoftheart methods, and thus, our study is of importance for future GNA method development.
Also, we note that although we already showed on the example of MIGRAAL and IsoRankN that combining NCF of one method and AS of another method can lead to a new superior method [2, 3], testing whether the same holds for MIGRAAL and GHOST, and in particular identifying the superior of the two NCFs, is of importance. First, validating that this also holds for MIGRAAL and GHOST would only further stress out the need to carefully design a strategy for evaluating a novel approach against existing ones. Simply comparing the approaches, as has typically been done, is not enough. A more advanced evaluation strategy, such as our mixandmatch approach, is more appropriate. Second, MIGRAAL’s NCF is a graphletbased measure of topological node similarity [37] that is also used by many other network aligners [2, 3, 5, 6, 38] or even network clustering methods [37, 39, 40] to link network topology with biological function. When a new measure of topological similarity appears that is also argued to successfully capture biological function, such as GHOST’s NCF, it is extremely important to fairly compare it against the graphletbased node similarity measure (which has not been done to date). In this way, future studies oriented towards learning new biological knowledge from network topology can focus on the most accurate node similarity measure. And this is exactly one of the goals of our study—to determine which of the two NCFs is superior. (We already demonstrated the superiority of MIGRAAL’s graphletbased NCF over IsoRankN’s popular PageRankbased NCF [2, 3].)
Our approach and contributions
MIGRAAL [7] and GHOST [25] are two stateoftheart global network aligners that injectively map nodes between two networks in a way that preserves topologically or functionally conserved network regions. The two methods are conceptually similar, in the sense that their NCFs assume two nodes from different networks to be similar if their topological neighborhoods are similar. However, the mathematical and implementation details of the two NCFs are different. The same holds for the two methods’ ASs. To evaluate the contribution to the alignment quality of each of the two NCFs and two ASs, we mix and match these, resulting in a total of four different combinations. We then use each combination to produce alignments for synthetic networks with known ground truth node mapping as well as for realworld networks without known ground truth node mapping, and we evaluate the quality of each alignment with respect to five topological and two biological alignment quality measures.
In general, we find that MIGRAAL’s NCF is superior to GHOST’s NCF, while the superiority of the methods’ ASs is datadependent. Hence, for those network data on which GHOST’s AS is superior to MIGRAAL’s AS, we propose the combination of MIGRAAL’s NCF and GHOST’s AS as a new superior network aligner.
While fairly evaluating MIGRAAL’s and GHOST’s NCFs and ASs, we approach two additional important research questions that, to our knowledge, have not been asked systematically in the context of network alignment thus far: (1) how much of the node similarity information within the NCF should come from protein sequence data compared to network topology data, and (2) how large the size of the neighborhoods of the compared nodes from different networks should be when generating topological similarity information within the NCF. Current GNA methods generally use a seemingly arbitrary amount of sequence information in their NCF, and also, they assume that the larger the size of a node’s neighborhood, the better the alignment quality. Thus, in this study, we evaluate whether these “stateoftheart” choices are actually appropriate. We note that the first question has been recognized in some of the existing work [25, 31, 41], but this question has not been systematically addressed to the same extent as in our study. To our knowledge, the second question has not been addressed at all thus far.
In general, we find that which amount of sequence information is used within NCF does not drastically affect neither topological or biological alignment quality, while the effect of topological information is drastic. Namely, using no topological information within NCF results in poor topological and sometimes even biological alignment quality. Hence, topology takes precedence over sequence when it comes to improving alignment quality. Also, we find that using larger network neighborhood sizes within NCF in most cases leads to better alignment quality than using smaller neighborhood sizes. However, it is not always the case that the largest neighborhood size is the best; in many cases, the second largest size is the best. Therefore, using this size instead of the largest one would drastically decrease computational complexity of the given method without decreasing its accuracy.
We note that a recent study [31] performed a valuable survey of a number of GNA methods, focusing in the process on ranking the different methods based on their performance. However, that study did not focus on indepth understanding why a given aligner performs the way it does, which is what we aim to do in our study. By analyzing a GNA method’s NCF and AS individually, we are able to understand the effect on alignment quality of each of the two algorithmic components. Furthermore, this existing study [31] compared the different methods with respect to a topological alignment quality measure called induced conserved structure (ICS) [25]. However, recently it was shown that ICS is an inappropriate measure of topological alignment quality, and a new superior measure was proposed, called symmetric substructure score (\(S^3\)) [30]. Here, we use the \(S^3\) measure, along with several additional measures, thus increasing the confidence in our results compared to the results reported in Clark and Kalita [31]. In addition, this existing study [31] evaluated the different network aligners only on realworld networks of different species, for which the ground truth node mapping is not known. Here, we do the same, and we also align a highconfidence biological network to its noisy counterparts (“Data sets”). In the latter case, the ground truth node mapping is known and we can thus measure how well each aligner reconstructs the node mapping [corresponding to node correctness (“Network alignment quality measures”)]. This important evaluation cannot be done when the actual node mapping is not known and was thus not carried out in Clark and Kalita [31], despite the fact that measuring node correctness is the most appropriate way of evaluating a network aligner’s accuracy [5–7, 25, 30] before applying the aligner to networks of different species to learn new biological knowledge. Moreover, this existing study [31] still arrived to the conclusion that GHOST is “an excellent performer”, despite the fact that many newer methods were involved into the comparison. Thus, our results showing that we can improve GHOST even further by using its AS on top of MIGRAAL’s NCF are an additional novel contribution of our study. Finally, we note again that in addition to providing comprehensive indepth evaluation of the two prominent network aligners (rather than simply comparing their performance as in Clark and Kalita [31]), we also study in detail the effect of different parameters (such as the amount of sequence information or neighborhood size considered within NCF) on the alignment quality; this was not done in the recent study [31].
Methods
Data sets
We use two popular benchmark sets of networks in this study: (1) synthetic networks with known ground truth node mapping and (2) realworld protein–protein interaction (PPI) networks without known ground truth node mapping [2, 3, 7, 25, 30].
The synthetic network data with known node mapping consists of a highconfidence yeast PPI network, which has 1,004 proteins and 8,323 PPIs [5–7, 25, 30, 42], and five additional networks that add noise to the yeast network. Noise is the addition to the yeast network of lowconfidence edges from the same data set [42], and each of the five additional noisy networks adds \(x\%\) noise to the original network, where \(x\) varies from 5 to 25% in increments of 5%. In this network set, we align the original yeast network to each of the synthetic networks with \(x\%\) noise, resulting in the total of five network pairs to be aligned.
The realworld PPI network data without known node mapping consists of PPI networks of the following four species: S. cerevisiae (yeast/Y), D. melanogaster (fly/F), C. elegans (worm/W), and H. sapiens (human/H). The yeast, fly, worm, and human networks have 3,321 proteins and 8,021 PPIs, 7,111 proteins and 23,376 PPIs, 2,582 proteins and 4,322 PPIs, and 6,167 proteins and 15,940 PPIs, respectively [43]. In this network set, we align PPI networks for each pair of species, resulting in the total of six network pairs to be aligned.
We note that the synthetic network data is not truly synthetic, as both the original yeast network and the noise in terms of the lowerconfidence PPIs come from an actual experimental study [42]. We refer to this network set as synthetic simply because we know the known ground truth node mapping, unlike for the realworld PPI network set. Also, we note that the synthetic network data encompasses “cocomplex” PPIs obtained by affinity purification followed by massspectrometry (AP/MS), among other PPI types, while the realworld PPI network data consists of “binary” yeast twohybrid (Y2H) PPIs. Another difference between the two network sets is that for the synthetic data the smaller (original yeast) network is an exact subgraph of the larger (noisy) network, whereas this is not the case for networks of different species in the realworld data.
When evaluating the amount of sequence data that should be used within NCF when generating an alignment, we use protein sequence similarity data. This data set comes from BLAST bitvalues from the NCBI database [44].
When evaluating the biological alignment quality with respect to functional enrichment of the aligned nodes, we use Gene Ontology (GO) annotation data from our recent study [2, 3].
Importantly, we note that we use the above data sources and versions of the data because the exact same data have already been used in the existing work, which allows for fair and consistent method evaluation. If the main focus of one’s work was to predict new biological knowledge rather than to conduct fair method evaluation and comparison, then we would recommend using the latest and thus most complete versions of the data.
Existing network aligners and their NCFs and ASs
MIGRAAL’s NCF
MIGRAAL improves upon its predecessors, GRAAL [5] and HGRAAL [6], by using the same NCF (see below) but by combining GRAAL’s and HGRAAL’s ASs into a new superior AS (see below).
MIGRAAL’s NCF relies on the concept of small induced subgraphs called graphlets (Figure 1) [37, 39, 40, 45–47]. All 2–5node graphlets are considered. Because of the smallworld nature of realworld networks, using larger graphlets would unnecessarily increase the computational complexity needed the count the graphlets [5, 6]. Based on the graphlets, the node graphlet degree vector (nodeGDV) is computed for each node in each network, which counts how many times the given node touches each of the 2–5node graphlets, i.e., each of their 73 node symmetry groups (or automorphism orbits; Figure 1). As such, nodeGDV captures up to a fourdeep network neighborhood of the node of interest. By comparing nodeGDVs of two nodes to compute their nodeGDVsimilarity, and by doing so between each pair of nodes in different networks, one is able to capture pairwise topological node similarities between the different networks.
MIGRAAL also allows for integration of other node similarity measures into its NCF, such as protein sequence similarity. Thus, MIGRAAL has the builtin functionality of allowing the user to incorporate their own custom pairwise node scores rather than rely on MIGRAAL’s NCF, which is exactly how we incorporate GHOST’s NCF as input into MIGRAAL’s AS.
MIGRAAL’s AS
GRAAL’s AS utilizes a seedandextend approach to greedily maximize the total NCF over all aligned nodes. HGRAAL, on the other hand, finds optimal alignments with respect to the total NCF by using the Hungarian algorithm to solve the linear assignment problem. MIGRAAL’s AS combines GRAAL’s greedy seedandextend approach with HGRAAL’s optimal AS into a superior AS.
Specifically, for graphs \(G\) and \(H\), MIGRAAL’s AS selects a pair of nodes \(u\) and \(v\), where \(u \in G\) and \(v \in H\), which have the highest similarity score among all pairs of nodes from the different networks. It then begins to align these nodes’ neighbors as follows. Let us denote by \(N_G(u)\) and \(N_H(v)\) the sets of neighbors of nodes \(u\) and \(v\), respectively. A bipartite graph is constructed using nodes from \(N_G(u)\) and \(N_H(v)\), where there exists an edge between a node \(x\) from \(N_G(u)\) and a node \(y\) from \(N_H(v)\) if and only if a neighbor of \(x\) has already been aligned to a neighbor of \(y\). A confidence weight (i.e., the NCFbased similarity between two given nodes) is then assigned to each edge. Given the resulting bipartite graph, MIGRAAL’s AS solves the maximum weight bipartite matching problem to determine which nodes in \(N_G(u)\) and \(N_H(v)\) should be aligned to each other. After MIGRAAL’s AS is done aligning nodes from \(N_G(u)\) to nodes from \(N_H(v)\), it then expands to these nodes’ neighbors and repeats the above steps to align them. The expansion continues iteratively until the entire smaller network is exhausted. For more details on MIGRAAL’s AS, see the original publication [7].
GHOST’s NCF
GHOST’s NCF takes into account a node’s \(k\)hop neighborhood \((k=4)\), which is the induced subgraph on all nodes whose shortest path distance from the node in question is less than or equal to \(k\) (Figure 2). Intuitively, GHOST’s NCF computes topological distance (or equivalently similarity) between two nodes from different networks by comparing the nodes’ “spectral signatures”. These signatures are based on the spectrum of the normalized Laplacian for subgraphs of radius \(k\) centered around a given node. Essentially, the spectral signature of a node is based on subgraph counts in the node’s khop neighborhood [25]. GHOST also allows for the incorporation of sequence information into its NCF, in which the resulting NCF is a linear combination of GHOST’s topological and sequence distance scores. For further details on GHOST’s NCF, refer to the original publication [25]. In our study, we consider \(k=1, 2, 3, 4\), which allows for a fair comparison of GHOST’s NCF to MIGRAAL’s NCF when varying the size of network neighborhood that is considered within the NCFs (“Aligners resulting from combining existing NCFs and ASs, and their parameters”).
GHOST’s AS
GHOST’s AS is also a seedandextend method, but unlike MIGRAAL’s AS that deals with the linear assignment problem, GHOST’s AS deals with the quadratic assignment problem (Figure 3 illustrates this). GHOST’s AS uses a twophase seedandextend strategy by first selecting nodes \(u\) and \(v\), where \(u \in G\) and \(v \in H\), which have the highest similarity score among all pairs of nodes from the different networks, and then extending around these nodes to align their neighbors [i.e., nodes from \(N_G(u)\) and \(N_H(v)\)]. To do this, GHOST’s AS considers pairwise similarities between nodes in \(N_G(u)\) and \(N_H(v)\) in addition to similarities between nodes within the same network, and all of these similarities are used to estimate a solution to the quadratic assignment problem, which is the node alignment. For further details on GHOST’s AS, refer to the original publication [25].
Aligners resulting from combining existing NCFs and ASs, and their parameters
Mixing and matching different NCFs and ASs
To fairly evaluate the two algorithmic components of MIGRAAL and GHOST, we aim to first compare the two NCFs under the same AS, for each of the two ASs. We then aim to compare the two ASs under the same NCF, for each of the two NCFs. This results in a total of four aligners, i.e., different combinations of the two methods’ NCFs and ASs. However, GHOST does not allow the user to import their own (e.g., MIGRAAL’s) NCF into its AS, so we are unable to study the combination of MIGRAAL’s NCF and GHOST’s AS. Thus, in total, we consider three different aligners (Table 1).
Varying the amount of sequence versus topological information within NCF
An additional goal of this paper is to determine the most appropriate amount of sequence information versus topological information to be included into NCF. Thus, for each aligner, we generate NCFs with varying amounts of sequence and topology information, as \(\alpha T + (1\alpha )S,\) where \(T\) represents topological similarity score (e.g. nodeGDVsimilarity) and \(S\) represents sequence similarity score. We vary \(\alpha \) from 0 to 1 in increments of 0.1.
Varying the size of network neighborhood within NCF
Further, we aim to determine the most appropriate neighborhood size that should be used within NCF when producing an alignment. Thus, for each aligner (and for each value of \(\alpha \)), we also consider four different neighborhood sizes, as described in Table 2. We note that although we have tried to classify under the same neighborhood size label (e.g. T1 in Table 2) graphlet sizes considered within MIGRAAL’s NCF and \(k\)hop values considered within GHOST’s NCF, it is not necessarily the case that the neighborhood of a node that is covered by graphlets of a given size and the neighborhood of the same node that is covered by the corresponding \(k\)hop value match exactly. That is, for example, 2–3node graphlets and 2hop neighborhood (both corresponding to T2 in Table 2) do not necessarily cover exactly the same amount of network topology. Yet, we have aimed to provide as accurate as possible classification in Table 2, in order to allow for as fair as possible comparison of the two methods’ NCFs under varying sizes of network neighborhoods.
Implementation details
The types of scores that MIGRAAL and GHOST take in as input are different: MIGRAAL looks at node similarities (the higher the score, the more similar the nodes), while GHOST looks at node distances (the lower the score, the more similar the nodes). We carefully take this into account to allow for fair method comparison. For example, to ensure that neither NCF has an advantage due to the format of the scores, we normalize all scores. That is, node similarity scores used in MIGRAAL can exceed the value one, while no scores generated by GHOST are greater than one. To make the two sets of scores comparable, we scale MIGRAAL’s node similarity scores to the [0–1] range by dividing each of the scores by the maximum similarity score. Because GHOST deals with distances rather than similarities, we take one minus GHOST's NCF and then plug in the resulting node scores into MIGRAAL's AS.
Further, MIGRAAL’s NCF returns all pairwise node similarity scores between two networks. However, GHOST’s NCF returns only a subset of all pairwise node distance scores, depending on the network size. To complete GHOST’s pairwise node score matrix and thus allow for it to be given as input into MIGRAAL’s AS, we assign a score equal to the highest distance score returned by GHOST to all node pairs for which GHOST did not return a distance score.
Finally, the current implementation of MIGRAAL’s AS does not function properly when a large pairwise node similarity matrix is plugged into it. Thus, MIGRAAL’s AS has had difficulty aligning the two largest networks from our study, the fly and human networks. As a solution, we create a matrix that contains only the top 21 million node similarity scores of the original node similarity matrix, this being the maximum that our computational resources would process. With this adjustment, we are successfully able to generate all flyhuman alignments.
Network alignment quality measures
We use well established network alignment quality measures [2, 3, 30]. Let \(G_1(V_1,E_1)\) and \(G_2(V_2,E_2)\) be two graphs such that \(V_1 \le V_2\). An alignment of \(G_1\) to \(G_2\) is a total injective function \(f : V_1 \rightarrow V_2\); every element of \(V_1\) is matched uniquely with an element of \(V_2\). Let us denote by \(E^{\prime}_2\) the set of edges from \(G_2\) that exist between nodes in \(G_2\) that are aligned by \(f\) to nodes in \(G_1\).
Topological evaluation
We use five measures of topological alignment quality:

1.
Node correctness (NC) If \(h : V_1 \rightarrow V_2\) is the correct ground truth node mapping between \(G_1\) and \(G_2\) (when such mapping is known), then NC of alignment \(f\) is: \(NC = \frac{\{u \in V_1 : h(u) = f(u) \}}{V_1} \times {100\%}\) [5]. This measure can be computed only for alignments of the synthetic network set with known ground truth node mapping (“Data sets”). All remaining measures (listed below) can be computed for the real network set with unknown node mapping as well.

2.
Edge correctness (EC) EC is the percentage of edges from \(G_1\), the smaller network (in terms of the number of nodes), which are aligned to edges from \(G_2\), the larger network [5]. Formally, \(EC = \frac{E_{1} \cap E'_2}{E_{1}}\times {100\%}, \) where the numerator is the number of “conserved” edges, i.e., edges that are aligned under the given node mapping. The larger the EC score, the better the alignment.

3.
Induced conserved structure (ICS) \(ICS = \frac{E_{1} \cap E'_2}{E'_2}\times {100\%}.\) EC might fail to differentiate between alignments that one might intuitively consider to be of different topological quality [25], since it is defined with respect to edges in \(E_1\). For example, aligning a \(k\)node cycle in \(G_1\) to a \(k\)node cycle in \(G_2\) would result in the same EC as aligning a \(k\)node cycle in \(G_1\) to a \(k\)node clique (complete graph) in \(G_2.\) Clearly, the former is intuitively a better alignment than the latter, since no edges that exist between the \(k\) nodes in \(G_2\) are left unaligned in the first case, whereas many edges are left unaligned in the second case. Since ICS is defined with respect to edges in \(E'_2\), it would have the maximum value of 100% when aligning a \(k\)node cycle to a \(k\)node cycle, and it would have a lower value when aligning a \(k\)node cycle to a \(k\)node clique [30]. The larger the ICS, the better.

4.
Symmetric substructure score (\(S^3\)) EC penalizes the alignment for having misaligned edges in the smaller network. ICS penalizes the alignment for having misaligned edges in the larger network. S\(^3\) on the other hand, aims to improve upon EC and ICS by penalizing for misaligned edges in both the smaller and larger network. S\(^3 = \frac{E_{1} \cap E'_2}{E_1 +E'_2  E_{1} \cap E'_2}\times {100\%}.\) For details, see the original publication [30].

5.
The size of the largest connected common subgraph (LCCS) [5], which we use for the following reason. Of two alignments with similar EC, ICS, or S\(^3\) scores, one could expose large, contiguous, and topologically complex regions of network similarity, while the other could fail to do so. Thus, in addition to counting aligned edges or nodes that participate in the aligned edges, it is important that the aligned edges cluster together to form large connected subgraphs rather than being isolated. Hence, we define a connected common subgraph (CCS) as a connected subgraph (not necessarily induced) that appears in both networks [6]. We measure the size of the largest CCS (LCCS) in terms of the number of nodes as well as edges. Namely, we compute the LCCS score as in our recent work [30]. First, we count \(N\), the percentage of nodes from \(G_1\) that are in the LCCS. Then, we count \(E\), the percentage of edges that are in the LCCS out of all edges that could have been aligned between the nodes in the LCCS. That is, \(E\) is the minimum of the number of edges in the subgraph of \(G_1\) that is induced on the nodes from the LCCS, and the number of edges in the subgraph of \(G_2\) that is induced on the nodes from the LCCS [30]. Finally, we compute their geometric mean as \(\sqrt{(}N \times E)\), in order to penalize alignments that have small \(N\) or small \(E\). Large values of this final LCCS score are desirable.
Biological evaluation
Only alignments in which many aligned node pairs perform the same function should be used to transfer function from annotated parts of one network to unannotated parts of another network [30]. Hence, we measure GO [48] enrichment of aligned proteins pairs, i.e., the percentage of protein pairs in which the two proteins share at least one GO term, out of all aligned protein pairs in which both proteins are annotated with at least one GO term. We refer to this percentage as GO correctness (GO). We do this with respect to complete GO annotation data, independent of GO evidence code. Also, since many GO annotations have been obtained via sequence comparison, and since some of the aligners use sequence information, we repeat the analysis considering only GO annotations with experimental evidence codes, in order to avoid the circular argument. In this case, we refer to GO correctness as experimental GO correctness (EXP). The higher the GO and EXP values, the better [30].
Results and discussion
We aim to answer the following three main questions in the context of network alignment: (1) which NCF and AS is superior to the other, and is there perhaps a combination of one existing method’s NCF and another existing method’s AS that is the superior aligner in terms of accuracy as well as time complexity (“What is the best NCF and the best AS?”)? (2) How much sequence versus topological information to use within NCF (“The amount of sequence versus topological information within NCF?”)? (3) How large the size of network neighborhoods of compared nodes to consider within NCF (“The size of nodes’ neighborhoods within NCF?”)? In addition, we comment on relationships between different alignment quality measures (“Relationships between different alignment quality measures”). Finally, we conclude in “Conclusions”.
What is the best NCF and the best AS?
By comparing MM and GM aligners, we can fairly compare the two NCFs under the same (MIGRAAL’s) AS. Also, by comparing GM and GG, we can fairly compare the two ASs under the same (GHOST’s) NCF. See “Aligners resulting from combining existing NCFs and ASs, and their parameters” for details on each aligner.
Synthetic networks with known node mapping
Overall, GHOST’s NCF is slightly superior to that of MIGRAAL (Figure 4a, b). Also, GHOST’s AS is superior to MIGRAAL’s AS (Figure 4a, b). However, these findings are based on all alignments (with known node mapping) for all values of \(\alpha \), all neighborhood sizes, and all measures of alignment quality combined (“Aligners resulting from combining existing NCFs and ASs, and their parameters”), which might not be fair. Thus, in Figure 5a–c, for each aligner, for each alignment quality measure, we show results for the best alignments over all values of \(\alpha \) and all neighborhood sizes, for three out of all five network pairs (for the remaining network pairs, see Additional file 1: Figures S1 and S2). Now, the general trend (and especially with respect to NC as the most accurate ground truth measure of alignment quality) is that the best scores for MM are either comparable or superior to those of GM, indicating slight superiority of MIGRAAL’s NCF over GHOST’s. Nonetheless, GG still always outperforms GM, indicating superiority of GHOST’s AS over MIGRAAL’s AS.
It is possible to break down the above results and study how the ranking of the different NCFs and ASs changes with the change in the value of \(\alpha \), which corresponds to the amount of topological similarity information used within NCF (Additional file 1: Figures S3–S7). In general, MIGRAAL’s NCF is comparable to GHOST’s NCF across all \(\alpha \) values, as MM and GM scores are similar. On the other hand, GHOST’s AS shows superiority over MIGRAAL’s AS, as GG consistently results in higher scores than GM. We note that we show that the value of \(\alpha \) does not greatly affect alignment quality (“The amount of sequence versus topological information within NCF?”).
It is also possible to break down the above results even further and study how the ranking of the different NCFs and ASs changes with the change in the neighborhood size that is considered within NCF (Additional file 1: Figures S3–S7). In general, for the smaller neighborhood sizes (T1 and T2), GHOST’s NCF generally produces comparable or superior results to MIGRAAL’s NCF, as GM scores are higher than MM scores. However, for the larger neighborhood sizes (T3 and T4), MIGRAAL’s NCF is comparable or superior to GHOST’s NCF. And because we show that the larger neighborhood sizes (T3 and T4) are overall superior (“The size of nodes’ neighborhoods within NCF?”), this means that overall MIGRAAL’s NCF is comparable to or superior to GHOST’s NCF. On the other hand, in general, for all network sizes, GHOST’s AS consistently outperforms MIGRAAL’s AS, as GG scores is typically higher than GM scores.
When comparing the different aligners with respect to computational complexity (rather than accuracy, as above), we find the following. Overall, GG is the fastest, followed by MM, followed by GM (Figure 6a). This implies that since MM is faster than GM, MIGRAAL’s NCF is less computationally intensive than GHOST’s NCF. Also, since GG is faster than GM, GHOST’s AS is less computationally intensive than MIGRAAL’s AS.
We note that in order to fairly compare the running times of all aligners used in this study, we run all aligners using neighborhood size T4 (Table 2). We cannot do this for the other (smaller) neighborhood sizes for the following reasons. While GHOST allows the user to specify any desired neighborhood size as input, MIGRAAL’s NCF does not. Namely, the current implementation of MIGRAAL by default computes all up to 5node graphlets (i.e., T4). Then, to get the information contained in up to 2, 3, or 4node only graphlets, one simply considers the relevant dimensions of the entire up to 5node graphlet degree vector and discards all other dimensions. Thus, we cannot evaluate the computational complexity of considering 2, 3, or 4node only graphlets, as with the current implementation, each of these options takes the same (longest) amount of time that computing up to 5node graphlets takes.
Real networks with unknown node mapping
Overall, unlike for the synthetic network data set with known node mapping, on the real network data set with unknown mapping, MIGRAAL’s NCF is now comparable or superior to that of GHOST (Figure 4c, d). Further, MIGRAAL’s AS is now comparable or superior to GHOST’s AS (Figure 4c, d). We confirm these findings even when we limit from all alignments (Figure 4c, d) to the best alignments only (just as above) (Figure 5d–f) (Additional file 1: Figures S8–S10).
When zooming into the results further to observe the effect of the \(\alpha \) parameter, in general, for all values of \(\alpha \), MIGRAAL’s NCF is comparable or superior to GHOST’s NCF and MIGRAAL’s AS is comparable to GHOST AS across all values of \(\alpha \) (Additional file 1: Figures S11–S16). The same holds independent on the neighborhood size that is considered within NCF (Additional file 1: Figures S11–S16).
When comparing the different aligners with respect to computational complexity (rather than accuracy, as above), we find the following. Unlike for the synthetic network data, we now observe that MM is significantly the fastest, followed by GM, followed by GG (Figure 6b). This implies that since MM is faster than GM, MIGRAAL’s NCF is less computationally intensive than GHOST’s NCF. Also, since GM is faster than GG, MIGRAAL’s AS is less computationally intensive than GHOST’s AS.
Summary
Which NCF or AS is the best overall is not easy to determine, as the results are datadependent. But when we limit analyses of each aligner to the best alignments over all parameters, MM is comparable or superior to GM, indicating that MIGRAAL’s NCF is better than GHOST’s NCF, while the performance of GM versus GG, i.e., of MIGRAAL’s AS versus GHOST’s AS, is still datadependent. These results hold not just in terms of accuracy but also in terms of computational complexity. We note that the reason why the performance of the two ASs is datadependent (GHOST’s AS performing better on the synthetic networks, and MIGRAAL’s AS performing better on the realworld networks) could be due to the differences of the two network data sets. Namely, recall that the synthetic network data encompasses “cocomplex” PPIs obtained by AP/MS, among other PPI types, while the realworld network data consists of “binary” Y2H PPIs (“Data sets”).
The above results imply that the graphletbased measure of topological node similarity [37] that MIGRAAL uses (along with many other network aligners [2, 3, 5, 6] or even network clustering methods [37, 39, 40]) remains the stateoftheart, as it is superior to the newer spectral signaturebased node similarity measure that GHOST uses (and especially to the PageRankbased node similarity measure that aligners from the IsoRank family use, as we already showed in our recent study [2, 3]). Our results indicate that the slight superiority of GHOST (i.e., GG) over MIGRAAL (i.e., MM) that was claimed in the original GHOST publication [25] seems to come from GHOST’s AS and not its NCF, which is not surprising, since GHOST’s AS deals with the quadratic assignment problem whereas MIGRAAL’s AS deals only with linear assignment problem. Further, our results indicate that the combination of MIGRAAL’s NCF and GHOST’s AS (i.e., MG) could be a new aligner that is superior to the existing MIGRAAL (i.e., MM) and GHOST (ie., GG) aligners on at least some data sets. Unfortunately, explicitly testing this is not possible with the current implementation of GHOST, as per our conversation with the authors of GHOST, the current implementation is too complex to modify to allow for plugging MIGRAAL’s (or any other method’s) NCF into GHOST’s AS.
The amount of sequence versus topological information within NCF?
Recall that we vary the amount of topological node similarity information within NCF with the \(\alpha \) parameter (where \(\alpha \) of 0 means that no topology information is used, i.e., that only sequence information is used, whereas \(\alpha \) of 1 means that only topology information is used; “Aligners resulting from combining existing NCFs and ASs, and their parameters”). Here, we study the effect of the \(\alpha \) parameter on alignment quality.
Synthetic networks with known node mapping
Overall, the value of \(\alpha \) does not affect alignment quality, as long as some amount of topological information is used. That is, only \(\alpha =0.0\) results in completely inferior alignments, especially with respect to topological alignment quality, whereas all other values of alpha are moreless comparable (Figure 7a, b).
It is expected that the larger the value of \(\alpha \), i.e., the more of topological information is used within NCF, the better the topological alignment quality. Again, this is exactly what we observe (Figure 7a). It is also expected that the smaller the value of \(\alpha \), i.e., the more of sequence information is used within NCF, the better the biological alignment quality. Surprisingly, this is not what we observe (Figure 7b): larger values of \(\alpha \) (e.g., 0.7) result in more of highquality alignments than \(\alpha =0\).
When zooming into the results further to observe the effect of the aligner, in general, we see the same trends as above independent of the aligner (Additional file 1: Figures S3–S7). Namely, the results from Figure 7a, b hold independent on which NCF or AS is used. Further, there is no difference in the results across the two NCFs (Figure 8a, b). There is only a minor difference in the results across the two ASs, in the sense that the results are somewhat more stable across different \(\alpha \)s for GHOST’s AS than for MIGRAAL’s AS (Figure 8b, c). Also, GHOST’s AS suggests that in addition to not using \(\alpha =0\) (i.e., sequence alone), one should not use \(\alpha =1\) either (i.e., topology alone); but other than that, the choice of \(\alpha \) still has no major effect (Figure 8c).
When zooming into the results from Figure 7a, b further to observe the effect of the neighborhood size, we see that the results hold independent of the neighborhood size (Additional file 1: Figures S3–S7).
Real networks with unknown node mapping
The results that we observe for the synthetic networks in general hold for this network set as well. Namely, \(\alpha =0\) results in the worst topological alignment quality, while the other \(\alpha \) values are somewhat comparable, with a slight dominance of the larger values, as expected (Figure 7c). Interestingly, for this network set, the lowest value of \(\alpha =0\) results in the most of highestscoring alignments with respect to biological alignment quality; yet, even the largest \(\alpha \)s often lead to good alignments with respect to biological alignment quality (Figure 7d).
When zooming into the results further to observe the effect of the aligner, as with synthetic networks, the general results from Figure 7c, d hold independent of the aligner for real networks as well (Additional file 1: Figures S11–S16). However, unlike for synthetic networks, for real networks we now see result stability across all NCFs and all ASs, and not just for GHOST’s AS. Also, GHOST’s AS no longer suggests that \(\alpha =1\) should not be used.
When zooming into the results from Figure 7c, d further to observe the effect of the neighborhood size, just as with the synthetic networks, we again see that the results hold independent of the neighborhood size (Additional file 1: Figures S11–S16).
Summary
Overall, at least some amount of topological information should be included within NCF, as this results in good topological as well as biological alignment quality. While \(\alpha =0.0\) may (but does not always) result in biologically highquality alignments, in every case it fails to produce topologically superior results. Thus, \(\alpha =0.0\) should not be used.
The size of nodes’ neighborhoods within NCF?
Intuitively, one would expect that the increase in the size of nodes’ network neighborhoods within NCF (i.e., in the amount of network topology) would result in higherquality alignments. However, this assumption has not been tested to date. Instead, the existing methods blindly use the largest neighborhood size that is allowed by available computational resources (that is, MIGRAAL uses all 2–5node graphlets, whereas GHOST uses \(k=4;\) “Aligners resulting from combining existing NCFs and ASs, and their parameters”). Thus, within each aligner, we vary the neighborhood size from T1 to T4 (Table 2) to systematically evaluate the effect of this parameter.
Synthetic networks with known node mapping
Overall, the larger the neighborhood size, the better the alignment quality, even though all neighborhood sizes except T1 can in some cases result in higherquality alignments than any other neighborhood size (Figure 9a, b). That is, for some values of network alignment parameters, smaller neighborhoods can produce higherquality alignments than larger neighborhoods, which is a surprising though not alarming result. It is possible for larger neighborhood sizes to produce lower quality alignments due to nodes in one network having denser, more complex neighborhoods than nodes in the other network. For example, two nodes \(u\) and \(v\) from different networks can have similar neighborhoods at size e.g., T2 but different neighborhoods at larger size e.g., T3, if e.g., the 3hop neighborhood of node \(v\) in one network is empty while the 3hop neighborhood of node \(u\) in another network is not. Thus, although larger network neighborhoods include more of the network topological information, they could also “confuse” the network signal, depending on the topology of the aligned networks, in which case smaller neighborhoods may be preferred.
When zooming into the results further to observe the effect of the aligner, the general trends from Figure 9a, b still hold independent of the aligner, but some fluctuations in the results exist (Additional file 1: Figures S17–S21). Namely, MM generally prefers T3 and T4 neighborhood sizes. GM prefers T2 in addition to T3 and T4, where T3 or T4 are actually inferior to T2 in some cases, depending on the noise level. GG performs well on of T1T4, with a slight preference of T3 or T4, depending on the noise level. See Figure 10a for an illustration.
When zooming into the results further to observe the effect of the \(\alpha \) parameter, general trends from Figure 9a, b are overall the same for all values of \(\alpha \) (Additional file 1: Figures S17–S21). The only exception is \(\alpha =0\), which should not be used in the first place (“Summary”).
Real networks with unknown node mapping
Unlike for the synthetic networks, the largest neighborhood size (T4) is now not overly dominant over the smaller network sizes. Specifically, for real network data set, it is T3 that is the most dominant, followed by T4 and T2, which are tied, and followed by T1, which is inferior (Figure 9c, d).
When zooming into the results further to observe the effect of the aligner, we see that each aligner has an interesting behavior (Additional file 1: Figures S22–S27). Namely, MM’s and GG’s preference on the neighborhood size is mainly dictated by the choice of species whose networks are aligned. For GM, in general, the larger neighborhood sizes are preferred; in some cases, depending on the species, GM prefers T3 more than other neighborhood sizes. See Figure 10b for an illustration.
When zooming into the results further to observe the effect of the \(\alpha \) parameter, just as for synthetic networks, the results from Figure 9c, d do not drastically change with the change of \(\alpha \) value (Additional file 1: Figures S22–S27).
Summary
In general, the larger the neighborhood size within NCF, the higher the alignment quality. However, it is not necessarily the case that the largest neighborhood size always produces the best alignments nor that it is always dominant to the smaller neighborhood sizes. This means that slightly smaller neighborhood sizes (and T3 in particular) might be desirable, as this could not only produce better alignments in some cases but also decrease the computational complexity of the given method.
Relationships between different alignment quality measures
We use a total of seven alignment quality measures: the ground truth NC measure that can only be measured in alignments of synthetic networks with known node mapping, four additional topological measures (EC, ICS, \({\rm S}^3\), and LCCS), and two biological measures (GO and EXP) (“Network alignment quality measures”). Here, we briefly comment on the relationship between the different measures.
NC significantly correlates with both topological and biological alignment quality measures (Figure 11a), which is encouraging. Further, for the synthetic network data set, it is also encouraging that all other measures significantly correlate well (Pearson correlation coefficient of at least 0.8), even though we see some clustering of the topological measures and also of the biological measures (Figure 11a). Interestingly, each of the two biological measures, GO and EXP, correlates better with some of the topological measures (e.g., EC) than with each other.
Unlike for the synthetic network data, for the real network data, the topological measures now correlate poorly with the biological measures (Pearson correlation coefficient of at most 0.2; Figure 11b). Importantly, this implies that for the real network data set it might be hard to produce an alignment that is of excellent quality both topologically and biologically. Also, while we again see clustering of the topological measures, the two biological measures now correlate weakly (Figure 11b), indicating that the choice of GO annotation data obtained by experimental evidence code matters (“Network alignment quality measures”).
The result differences between the synthetic networks and the real networks could be due to differences in their properties (“Data sets”).
Note that when measuring the correlations between the different alignment quality measures, we have used the Pearson correlation coefficient. In case that the data is not necessarily normally distributed, using a nonparametric (i.e., distributionfree) measure of correlation would be appropriate. Hence, we repeat the above analysis with respect to such a measure, namely the Spearman correlation coefficient. Importantly, our results produced in this way are mostly consistent to the results produced when using the Pearson correlation coefficient (Additional file 1: Figure S28).
Conclusions
We have aimed to systematically answer three questions in the context of MIGRAAL and GHOST network aligners: (1) what is the contribution of each method’s NCF and AS to the alignment quality, (2) how much sequence versus topology information should be used within NCF when generating an alignment, and (3) how large the size of the neighborhoods of the compared nodes from different networks should be. Our results show that: (1) MIGRAAL’s NCF is superior to GHOST’s, while the performance of their ASs is datadependent, (2) some amount of topological data should be used in the NCF, and (3) the larger the amount of topology, the better, although using the second largest neighborhood size can result in better results and lower computational complexity compared to using the largest neighborhood size. Our results represent a set of general recommendations for a fair evaluation of any GNA method (and especially if the method falls into the twostate NCFAS category), not just MIGRAAL and GHOST.
Genomic sequence alignment has revolutionized our biomedical understanding. Biological network alignment has already had similar impacts. And given the tremendous amounts of biological network data that continue to be produced, network alignment will only continue to gain importance. The hope is that it could lead to new discoveries about the principles of life, evolution, disease, and therapeutics. Network alignment has also strived in other domains as well, with applications such as semantically matching entities in different ontologies [8] or comparing online social networks with impacts on user privacy [9].
Abbreviations
 GNA:

global network aligner
 LNA:

local network aligner
 NCF:

node cost function
 AS:

alignment Strategy
 PPI:

protein–protein interaction
 GDV:

graphlet degree vector.
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Authors’ contributions
JC participated in the design of the study, performed most of the analyses presented in the paper, helped analyze the results, and helped write the paper. YS performed the rest of the analyses (and those concerning MM aligner in particular) and helped write the paper. TM designed and supervised all aspects of the study, analyzed the results, and wrote the paper. All authors read and approved the final manuscript.
Acknowledgments
We thank Dr. R. Patro and Dr. C. Kingsford for their assistance with running GHOST. This work was supported by the National Science Foundation CAREER CCF1452795, CCF1319469 and EAGER CCF1243295 Grants.
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Competing interests The authors declare that they have no competing interests.
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Supplementary material containing additional results.
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Crawford, J., Sun, Y. & Milenković, T. Fair evaluation of global network aligners. Algorithms Mol Biol 10, 19 (2015). https://doi.org/10.1186/s1301501500508
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Keywords
 Protein–protein interaction networks
 Network alignment
 Network similarity
 Acrossspecies protein function prediction