Abstract
A deluge of new data on realworld networks suggests that interactions among system units are not limited to pairs, but often involve a higher number of nodes. To properly encode higherorder interactions, richer mathematical frameworks such as hypergraphs are needed, where hyperedges describe interactions among an arbitrary number of nodes. Here we systematically investigate higherorder motifs, defined as small connected subgraphs in which vertices may be linked by interactions of any order, and propose an efficient algorithm to extract complete higherorder motif profiles from empirical data. We identify different families of hypergraphs, characterized by distinct higherorder connectivity patterns at the local scale. We also propose a set of measures to study the nested structure of hyperedges and provide evidences of structural reinforcement, a mechanism that associates higher strengths of higherorder interactions for the nodes that interact more at the pairwise level. Our work highlights the informative power of higherorder motifs, providing a principled way to extract higherorder fingerprints in hypergraphs at the network microscale.
Introduction
Over the last two decades, networks have emerged as a powerful tool to analyze the complex topology of interacting systems^{1}. From social networks to the brain, several systems have been represented as a collection of nodes and links, encoding dyadic interactions among pairs of units. Yet, growing empirical evidence is now suggesting that a large number of such interactions are not limited to pairs, but rather occur in larger groups^{2,3}. Examples include collaboration networks^{4}, human facetoface interactions^{5}, species interactions in complex ecosystems^{6}, cellular networks^{7} and structural and functional brain networks^{8,9}.
To properly encode such higherorder interactions^{2,3}, richer mathematical frameworks such as hypergraphs^{10} are needed, where hyperedges describe interactions taking place among an arbitrary number of nodes. To characterize these higherorder systems^{2}, computational tools from algebraic topology have been proposed^{11,12}, as well as generalization of common network concepts, including centrality measures^{13,14}, directedness^{15}, clustering^{16,17} and assortativity^{18}. An explicit treatment of higherorder interactions, including their inference and reconstruction^{19}, is necessary to understand network formation mechanisms^{20,21,22,23}, fully capture the real community structure of higherorder systems^{24,25,26} and extract their statistically validated higherorder backbone^{27}. Noticeably, taking into account higherorder interactions might be crucial to understand the emergent behavior of complex systems, as they have been found to profoundly impact diffusion^{28,29}, synchronization^{30,31,32,33,34}, social^{35,36,37} and evolutionary^{38} processes.
Networked systems may be differentiated by their preferential patterns of connectivity at the microscale, encoding a characteristic fingerprint often relevant for system functions. This may be quantified by measuring network motifs, small connected subgraphs that appear in an observed network at a frequency that is significantly higher than in a randomgraph null model^{39}. The analysis of the motifs of a network revealed the emergence of “superfamilies” of networks, i.e., clusters of networks that display similar local structure. These clusters tend to group networks from similar domains or networks that have evolved via similar evolutionary processes^{40}. In fact, motifs can be interpreted as elementary computational circuits, with specific functionalities that can be shared by similar networks. For example, transportation networks are designed to simplify the traffic flow, whereas gene regulation and neuron networks are often thought to be evolved to process information. These functional differences in such systems are reflected in the emergence of different significant motifs in the networks that describe them. In this regard, studying motifs can also give new insights into the dynamics and resilience of classes of networks^{40,41}. To explicitly uncover the relation between the dynamical processes that unfold on a network and its structural decomposition at the local scale, recently a refined notion of process motifs has been proposed^{42}, introducing a framework to assess the contribution of each motif to the overall dynamical behavior of the system.
Network motifs have been used in a wide range of applications. In biology, motifs have been extensively studied for the analysis of transcription regulation networks (i.e., networks that control gene expression). Studies show that diverse organisms from bacteria to humans exhibit common regulation patterns, each with its very own function in determining gene expression^{43,44,45,46,47}. Similarly, motif analysis has been applied to show how complex and flexible neural functions emerge from the composition of fundamental circuits in brain networks^{48}. Moreover, motifs have also been used as a feature for the identification of cancer^{49}. Eventually, the need to analyze biological datasets of everincreasing size has been a strong motivation for the development of more efficient algorithms^{50}. Besides biology, motifs have also been applied to provide fingerprints of the local structures of social networks^{51,52}, for the early detection of crisisleading structural changes in financial networks^{53} and to study the networks of direct and indirect interactions across species in ecology^{54,55}.
The interest of the research community in extracting fingerprints at the network microscale of realworld systems has led to considering richer frameworks for motif analysis^{56}, including extensions to more general network models such as weighted^{57}, temporal^{58} and multilayer^{59} networks. Weighted networks can be characterized in terms of the intensity and coherence of the link weights of their subgraphs^{60}. Temporal networks can be studied at both topological and temporal micro and mesoscale by considering timerestricted patterns of interactions^{61,62}. Statistically overexpressed small multilayer subgraphs^{63} highlight the local structure of multilayer networks such as the human brain^{64}. Nevertheless, the methods, algorithms and tools proposed in literature so far mostly consider only patterns of pairwise interactions, thus limiting our capabilities of characterizing the local structure of systems that involve group interactions. Recently, Lee et al.^{65} made the first contribution to close this gap: at difference with traditional motif analysis that focuses on patterns of interactions among small sets of nodes, they investigated patterns associated with connected hyperedges, in particular the 26 possible ways in which 3 connected hyperedges can overlap, allowing to extract information on the design principles of hypergraphs.
In order to systematically study the local structure of higherorder networks, here we investigate higherorder network motifs by providing a general and scalable methodology that naturally generalize to hypergraphs the seminal notion and analysis of network motifs proposed by Milo et al.^{39} for traditional graphs. Higherorder network motifs are defined as statistically overexpressed connected subgraphs of a given number of nodes, which can be connected by higherorder interactions of arbitrary order. We propose a combinatorial characterization of these new mathematical objects and develop an efficient algorithm to evaluate the statistical significance of each higherorder motif on empirical data. We show that we are able to extract fingerprints at the network microscale of higherorder realworld systems, and highlight the emergence of families of systems that show a similar higherorder local structure. Finally, we propose a set of measures to investigate the nested structure of hyperedges (i.e., the collection of lowerorder hyperedges defined on a subset of the nodes of a hyperedge) and provide evidence of the phenomenon of structural reinforcement, for which realworld group interactions are stronger if they are supported by a rich nested structure of pairwise interactions.
Results
Motif analysis has established itself as a fundamental tool in network science to extract fingerprints of networks at the microscale and to identify their structural and functional building blocks. By directly extending the traditional definition of network motifs, we can define higherorder network motifs as small connected patterns of higherorder interactions that appear in an observed hypergraph at a frequency that is significantly higher than a suitably randomized system.
Similarly to what happens with traditional motifs, the steps required to perform a higherorder motif analysis are (i) counting the frequency of each higherorder motif in a network, (ii) comparing the frequency of each motif with that observed in a null model, and (iii) evaluating their over or underexpression using a statistical measure. Algorithms for counting traditional motifs fail to capture information about group interactions, since they do not consider patterns of hyperedges. A detailed description of our proposal for algorithms and tools able to extract and evaluate higherorder motifs is reported in the Methods section.
For our motif analysis of realworld higherorder systems, we collected a number of freely available networked datasets. The datasets^{16,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82} come from a variety of domains: sociology (proximity contacts, votes), technology (emails), biology (gene/disease, drugs) and coauthorship. Each dataset has been manually tagged and associated to a specific domain. The description of each dataset is reported in Supplementary Note 1. In some datasets, higherorder structures are naturally encoded as hyperedges (e.g., three authors collaborating on the same paper), in others we infer higherorder structures from pairwise interactions (e.g., for facetoface interactions recorded over time, we promote cliques of size k to hyperedges of order k if the corresponding three dyadic encounters happened at the same time. We note that the choice of the specific timewindow for aggregation does not affect our results, as presented in Supplementary Note 2.
Combinatorial analysis of higherorder motifs
The number of possible patterns of pairwise undirected interactions involving three connected nodes is only two; however, it grows to six when considering also higherorder interactions (Fig. 1a). Finding an analytical form encoding the dependence of the number of higherorder motifs on the motif order k is a challenging task due to the constraints related to the computation of all possible combinations of higherorder interactions among k nodes. However, we are able to compute upper and lower bounds for this number. We denote with m the number of all the possible nonisomorphic connected hypergraphs of k vertices (we recall that two hypergraphs are isomorphic if they are identical modulo relabeling of the vertices). To compute an upper bound on m, we can count the number of labeled hypergraphs ignoring the constraint on being nonisomorphic and connected. There are \(\left(\begin{array}{c}k\\ i\end{array}\right)\)possible hyperedges of size i over k vertices. We are interested only in the hyperedges with cardinality at least 2; therefore, there are \(\mathop{\sum }\nolimits_{i = 2}^{k}\left(\begin{array}{c}k\\ i\end{array}\right)={2}^{k}k1\) possible hyperedges. When creating a labeled hypergraph we can either include each hyperedge or not, this yields a total number of possible labeled hypergraphs equal to \({2}^{{2}^{k}k1}\). To compute the lower bound of m, we construct connected hypergraphs on k vertices as follows. First, we pick any chain of edges and put all the edges in the hypergraph. This uses k − 1 edges and makes sure the hypergraph is connected. There are (2^{k} − k − 1) − (k − 1) = 2^{k} − 2k potential edges left over. Each of those edges can be added or not to the hypergraph, yielding at least \({2}^{{2}^{k}2k}\) connected hypergraphs. However, we have to count only nonisomorphic copies, and have so far counted labeled graphs. For each unlabeled graph, there are at most k! ways of labeling the vertices. So the number of nonisomorphic connected hypergraph is at least \(\frac{{2}^{{2}^{k}2k}}{k!}\). Figure 1b shows the upper and lower bounds on the growth of the possible higherorder motifs as a function of the order, as well as the exact count for small orders, showing that this function has a superexponential growth. The combinatorial explosion of higherorder motifs makes their storing and indexing in memory (required steps for counting their occurrences in empirical hypergraphs and evaluating their over or underexpression) intractable for high orders. Given these combinatorial difficulties, in the following, we focus on the analysis of the higherorder motifs of order 3 and 4.
Motifs of order 3
The over and underexpression measures of each higherorder motif (abundance with respect to a null model, see Methods) in a hypergraph are concatenated in a significance profile (SP, see Methods) that constitutes a fingerprint of the local structure of the network. In this section, we characterize the local connectivity of empirical networks at the smallest scale, with higherorder motifs of order 3.
After having computed the SPs of all the datasets, a first question one could ask is how hypergraphs from different domains differ on average in their SPs. We compute the SPs of a domain by grouping and averaging the SPs of all networks that belong to it (more information about the disagreggated SPs can be found in Supplementary Note 4). The analysis of the higherorder profiles of order 3 of each domain highlights the relative structural importance of certain patterns of higherorder interactions (Fig. 2a). The pairwise triangle II appears to be a highly overexpressed motif in all the domains, whereas the greatest differences across domains emerge from motifs that involve a 3hyperedge and at least one dyadic edge. In the social and technological domains, the motif VI made by a 3hyperedge and a triangle of dyadic edges is highly overexpressed, suggesting that entities interacting in groups also tend to interact individually. In coauthorship networks, the most overexpressed motifs are IV and V, which involve a 3hyperedge and one or two dyadic edges, indicating that in these domains there might be a hierarchical structure that prevents all nodes from interacting equally in pairs, as in the case of a research leader that coauthors papers with students and postdocs while the latter do not coauthor papers without the former. A similar motif is also found to be overexpressed in biological systems. Moreover, SPs allow also to analyze antimotifs, i.e., motifs that are highly underexpressed. An antimotif in the social and technological domains is III, the 3hyperedge without any dyadic interaction, indicating that it is unlikely that an interaction in the group is not followed or preceded by any pairwise interaction. The biological and coauthorship domain do not display any antimotif.
Another interesting question is whether the domain categorization naturally emerges from individually clustering the SPs of all the empirical hypergraphs. We perform a hierarchical cluster analysis considering the pairwise correlation between the distributions of the occurrences of the higherorder motifs for each dataset as (the inverse of) a distance (Fig. 2b). The analysis shows the emergence of two main clusters, i.e., families of higherorder networks that share similar patterns of higherorder interactions at the microscale. The clusters, here inferred in a purely datadriven manner, reproduce the partitions of domains displayed in Fig. 2a (social and technological datasets in a cluster, biological and coauthorship ones in the other), offering a more nuanced view on the similarity across datasets.
Motifs of order 4
In the previous section, we have systematically investigated the smallest higherorder motifs. The number of possible patterns of higherorder interactions involving 4 nodes is significantly higher than the corresponding with 3 nodes, as it grows from 6 to 171. Despite the difficulties associated to this increase, analyzing higherorder motifs of order 4 provides more nuanced information about the local structure of networks compared to 3motifs.
In Fig. 3a, we group together similar domains based on the analysis in the previous section showing the average of their SPs with the higherorder motifs of order 4. The order of motifs along the xaxis maximizes the visual difference in SPs across clusters. On the left end of the xaxis, we find motifs that are highly overexpressed in the Bio/Coauth domain, while they are underexpressed in the Socio/Tech domain. Conversely, on the right end of the xaxis, we find motifs that are overexpressed in the Socio/Tech domain, while not characteristic for the other domain. This observation suggests that both the extremes of the xaxis carry information about the structural differences among the clusters.
The richer structural information captured by the higherorder motifs of order 4 compared to their counterparts of order 3 is highlighted in the clustering analysis (Fig. 3b). When focusing on the two main clusters, the results are comparable with the previous cluster analysis. However, a richer hierarchical intracluster organization naturally emerges, as well as a better separation between the two clusters (See Supplementary Note 3).
Finally, we characterize the Socio/Tech and the Bio/Coauth clusters by means of their most overexpressed, and therefore most representative, higherorder motifs of order 4 (Fig. 3c). The Socio/Tech domain shows an overexpression of structures involving more lowerorder nested relations (e.g., dyadic links), while the Bio/Coauth domain displays a preference toward less relations but of higherorder. This pattern might be caused by the fact that people interacting in groups are likely to interact also in single pairs, therefore it is plausible that group interactions in the Socio/Tech domain are supported by a large number of lowerlevel interactions. On the other hand, people tend to write papers in large groups and tend to maintain the same research group over time, with few additions or removals. Therefore, patterns involving only dyadic relations are penalized. For a more indepth description of the most over and underexpressed higherorder motifs of order 4, we refer to Supplementary Note 5.
Nested organization of higherorder interactions
We now turn our attention to characterize the nested structure of large higherorder hyperedges. We define the nested structure of a large hyperedge h as the collection of hyperedges existing on a subset of the nodes of h, and extract statistics on the nested structure of hyperedges of any size. The advantage of this approach is that it still provides information about the local structure of submodules of a network, while its computational complexity is only linear in the number of hyperedges in the hypergraph.
First, we consider the average number of edges in the nested structures of hyperedges of different sizes (Fig. 4a). The networks are grouped according to their domain. While biological and coauthorship networks do not display evident differences in the number of nested edges with the growth of the hyperedge size, social and technological networks show a clear growing trend with a change of slope after orders 5 and 6.
In order to complement this information, we looked at how the mean size of the nested edges changes with the growth of the size of the analyzed hyperedges (Fig. 4b). In this case, all the domains show a growing trend, with biological and coauthorship networks displaying a faster growth. Thus, while social and technological networks tend to have more edges in the nested structure of their large hyperedges, they tend to be of small size. Biological and coauthorship networks, instead, shows an opposite behavior. All in all, this suggests that, in agreement with our previous findings, also at higher scales Socio/Tech network motifs are systematically more nested.
Higherorder motifs and reinforcement
In order to understand if and how the occurrence of nested dyadic interactions affects the strength of group interactions, we investigate how much the weight of each hyperedge (i.e., the number of times each group interaction occurs) is correlated with the number of nested pairwise links. We find that a positive trend emerges, indicating the existence of a correlation between a rich nested pairwise structure and the weight of a hyperedge (Fig. 5a). We dubbed this phenomenon, similar to the one highlighted in ref. ^{59} for multilayer networks, as higherorder structural reinforcement.
Moreover, we used the metadata about personal relationships between students recorded in the High School dataset from SocioPatterns to understand if similar reinforcing behavior is observed in the presence of friendship interactions between individuals. Friendship data have been collected in two ways, from Facebook accounts and through a questionnaire. In the first case, two students are always reciprocally friends, while in the second case a friendship can be unreciprocated. In Fig. 5b, we analyze the relationship between the average number of friends (both on Facebook and by questionnaire) and the topology of the different motifs in the proximity hypergraph. Our results show that the higher the number of pairwise interactions between students that interact in hyperedges of size three, the higher will be the number of friends in the group, further suggesting the existence of reinforcement mechanisms.
Discussion
The framework of network motifs is widely recognized as a fundamental tool for the analysis of complex networks. Able to highlight local structural characteristics of networks and influence their dynamics, motifs can be considered the fundamental building blocks of networks, and have produced applications in a number of fields such as biology and social network analysis.
Modeling complex systems by means of hypergraphs have recently emerged as a fundamental tool in Network Science, prompting the question of how to identify and assess network motifs in the presence of higherorder interactions. With the aim of extracting the local fingerprint of hypergraphs, in this work we introduced the notion of higherorder network motifs, which are small, possibly overlapping patterns of higherorder interactions that are statistically overexpressed with respect to a null model. We proposed a combinatorial characterization of higherorder network motifs, as well as an efficient algorithm to evaluate their statistical significance on empirical data. These tools allowed us to extract fingerprints of a variety of realworld systems by focusing on their characteristic patterns of higherorder interactions among small groups of nodes, showing the emergence of families of hypergraphs characterized by similar local structures. Moreover, we proposed a set of measures to study the nested structure of hyperedges and provided evidence of a structural reinforcement mechanism that associates stronger weights of higherorder interactions to groups of nodes that interact more at the pairwise level.
Similarly to the case of traditional pairwise network motifs, we believe that higherorder network motifs can pave the way to applications in a number of domains, pushed by the growing awareness of the relevance of the higherorder nature of interactions in many realworld systems. Given the possible applications of this framework in dataintensive domains, a limitation of our proposed approach is its scalability. In this work, indeed we proposed an algorithm that allows us to perform an exhaustive search, and for this reason, focuses on higherorder network motifs of size 3 and 4. However, we believe that there is room for different approaches, which sacrifices exhaustiveness but could allow us to gain deep insights on motifs of greater size. As the first step in this direction, we looked at the nested structure of patterns of hyperedges of larger orders. In addition to this, we believe that the development of sampling methods for the statistical evaluation of higherorder network motifs will be critical for more widespread realworld applications. All in all, our work highlights the informative power of higherorder motifs, providing an initial approach to extract higherorder fingerprints in hypergraphs at the network microscale.
Methods
A higherorder motif analysis involves three steps: (i) counting the frequency of each target higherorder motif in an observed network, (ii) comparing them with those of a null model, and (iii) establish the over or underexpression of certain subhypergraph patterns.
Here, we propose an exact algorithm to count the frequency of each higherorder motif of order k in a hypergraph. The first fundamental subtask to solve efficiently is the hypergraph isomorphism problem (i.e., establishing the equivalence under relabeling of two hypergraphs). In fact, for each occurrence of a connected subhypergraph with k nodes, we need to update the frequency of the respective higherorder motif of order k. This problem can be solved efficiently by enumerating and indexing all the higherorder motifs of order k with all the respective relabelings, allowing to update and count occurrences of patterns of subhypergraphs in constant time via a hash map. Since we are interested only in patterns of size 3 and 4, this is doable. In fact, the number of possible nonisomorphic patterns of higherorder interactions involving 4 nodes is 171, a number that makes all the relabelings storable in memory.
To enumerate subhypergraphs of size k we use an algorithm that proceeds in a hierarchical way. It first iterates over all the hyperedges of size k, which are able to directly induce a motif, i.e., a hyperedge of size k gives all the nodes to construct a motif of order k. Then it iteratively considers hyperedges of lower orders until it reaches the traditional dyadic links. Since hyperedges of order lower than k are not able to directly induce a motif, the algorithm proceeds in a way similar to^{83} and selects the remaining nodes by considering the neighborhood of the subhypergraph. Once selected k nodes, to efficiently construct their induced subhypergraph, we iterate over the power set of the k nodes (which corresponds to 2^{k} possible hyperedges) and keep only the hyperedges that exist in the original hypergraph.
As a null model, we use the configuration model proposed by Chodrow^{21}. We sample from the configuration model n = 100 times and compute the frequencies of the higherorder motifs in each sample. To validate the over and underexpression of certain patterns, we use the abundance Δ_{i} of each motif i relative to random networks proposed in^{40},
Following^{40}, we set ϵ = 4.
We define the SP of a network as the vector of Δ_{i} normalized to length 1,
Data availability
The datasets analyzed in this paper are available at https://github.com/FraLotito/higherordermotifs.
Code availability
The code for higherorder motif analysis is available at https://github.com/FraLotito/higherordermotifs.
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Acknowledgements
F.B. acknowledges partial support from the ERC Synergy Grant No. 810115 (DYNASNET).
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Q.F.L., F.M., A.M., and F.B. designed research; Q.F.L. analyzed data, developed the algorithms and performed the computations; Q.F.L., F.M., A.M., and F.B. analyzed results and wrote the paper.
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Lotito, Q.F., Musciotto, F., Montresor, A. et al. Higherorder motif analysis in hypergraphs. Commun Phys 5, 79 (2022). https://doi.org/10.1038/s42005022008587
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DOI: https://doi.org/10.1038/s42005022008587
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