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Identifying Representative Network Motifs for Inferring Higher-Order Structure of Biological Networks

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Identifying Representative Network Motifs for Inferring Higher-Order Structure of Biological Networks bioRxiv preprint doi: https://doi.org/10.1101/411520; this version posted September 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Identifying Representative Network Motifs for Inferring Higher-order Structure of Biological Networks Tao Wang y Jiajie Peng y School of Computer Science and Technology School of Computer Science Harbin Institute of Technology Northwestern Polytechnical University Harbin, China Xi’an, China wangtao [email protected] [email protected] Yadong Wang ∗ Jin Chen ∗ School of Computer Science and Technology Institute for Biomedical Informatics Harbin Institute of Technology University of Kentucky Harbin, China Lexington, USA [email protected] [email protected] Abstract—Network motifs are recurring significant patterns graph clustering [3], [6], [7]. For example, the studies on the of inter-connections, which are recognized as fundamental units C. elegans neuronal network and airport network have revealed to study the higher-order organizations of networks. However, the information propagation unit and the hub structure [3]. the principle of selecting representative network motifs for local motif based clustering remains largely unexplored. We present a Network motifs have been found in various natural networks scalable algorithm called FSM for network motif discovery. FSM such as the networks of biochemistry, neurobiology, and accelerates the motif discovery process by effectively reducing the ecology [1], [4]. By definition, network motifs are recurring number of times to do subgraph isomorphism labeling. Multiple subgraphs that appear more frequent in a target network than heuristic optimizations for subgraph enumeration and subgraph in random networks [8]. A network motif is supposed to be classification are also adopted in FSM to further improve its performance. Experimental results show that FSM is more biological or statistical significant [4]. Network motifs with a efficient than the compared models on computational efficiency similar structure are considered as building blocks to provide and memory usage. Furthermore, our experiments indicate that a specific function (such as information passing) for complex large and frequent network motifs may be more appropriate to systems [1]. Studying network motifs is effective towards be selected as the representative network motifs for discovering understand the system of biological interactions locally. How- higher-order organizational structures in biological networks than small or low-frequency network motifs. ever, it remains an open question which network motifs are the Index Terms—biological network, network motif, higher-order most appropriate for local higher-order graph clustering. More organization precisely, will using larger network motifs lead to improved clustering and knowledge discovery in networks? Or is a I. INTRODUCTION sparse network motif more capable of identifying higher-order Complex systems exhibit rich and diverse higher-order patterns in peripheral regions of a network? While network organizational structures and biological organisms are no ex- motifs have been recognized as fundamental units to analyze ception [1]–[3]. Studying network structures, esp. the higher- complex networks, the principle of network motif selection order network connectivity patterns, is critical for elucidat- remains largely unexplored. ing the fundamental mechanisms that control and mediate To systematically explore the network motif selection prob- complex behaviors in a biological system [1], [4], [5]. The lem, we face two computational challenges. First, given a most common higher-order structures are small to meso-scale biological network with thousands of vertices, the possible subgraphs of a network, which are referred to as network number of network motifs increases exponentially with the motifs [1]. Based on known network motifs, tools (such as increase of network motif size (i.e. the number of vertices) [9]. SNAP and MAPPR) have been developed to study network For example, the total number of size-5 network motifs in organization in a number of networks using local higher-order the C. elegans neuronal network is 3,509, while the number for size-6 network motifs is increased to 66,700. It is prac- This project is supported by NSF ABI (grant no. 1458556), DOE BES tically impossible to exhaustively test all possible network (grant no. DEFG0291ER20021), and NSFC (grant no. 61702421, 61332014). y Equal contributor motifs ranging from small to meso-scale for local higher-order ∗ to whom correspondence should be addressed. graph clustering [7]. Furthermore, a vast number of motif- bioRxiv preprint doi: https://doi.org/10.1101/411520; this version posted September 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. based clusters would be produced if many network motifs are tested, for which it is difficult to determine which one is the most appropriate. New strategies to optimize the selection of network motifs for inferring higher-order organizations in networks are required. The second challenge of local higher-order graph clustering lies on the network motif discovery problem itself. Tools have been developed for identifying network motifs [8], [10]–[19]. However, mining network motifs is still a computationally challenging task, provided that it has to enumerate subgraphs and count their frequencies in a target network and in numer- ous randomized networks. In particular, to match a subgraph instances, the Nauty algorithm [20] is often employed for graph isomorphism testing, of which O(k!) is the complexity in the worst case (k is the subgraph size) [21]. It is common to spend days to derive small to meso-scale network motifs Fig. 1. The adjacency matrix string (AMS), iterative adjacency matrix string from biological networks with thousands of vertices [8], [18]. (IAMS) and canonical adjacency matrix (CAM) of a size-4 subgraph. (A) A directed subgraph. (B) The adjacency matrix of the instance and its In summary, a holistic solution is clearly needed to identify corresponding AMS. (C) The subgraph enumeration and IAMS generation or select representative network motifs for studying higher- process starting with an empty subgraph g. Gray circle represents a new order organizations of biological networks. This work requires vertex vn added to g in each iteration. For each existing vertex vi of g, the corresponding value in IAMS has the following meanings: 0 for no edge to quickly obtain enough number of network motifs and between vn and vi, 2 for both edges (vn; vi) and (vi; vn), −1 for edge to analyze the motif-dependent organizational patterns using (vn; vi), and 1 for edge (vi; vn). (D) The CAM of the subgraph in (A). hypothesis-driven approaches. In this article, we present a new tool called “Fast Scalable motifs result in unreliable higher-order structures due to the Motif” (FSM) algorithm, with which, we make three hypothe- lack of subgraph instances? To our knowledge, the organi- ses on the indentification of representative network motifs and zational network pattern conservation problem has not been test them systematically. First, since frequent network motifs extensively studied. Applying FSM on the C. elegans neuronal have more instances in the target network than low frequent network with 131 vertices and 764 edges [1], we systemati- ones, they may derive less random clusters. We hypothesize cally analyzed the clusters obtained with different types of that a representative network motif must be highly frequent network motifs. The experimental results indicate that large (H1). Second, the instances of highly similar network motifs and frequent network motifs may be more appropriate to be may be densely overlapped with each other, leading to highly selected as the representative network motifs for discovering conserved organizational patterns. We hypothesize that if a higher-order organizational structures in biological networks. organizational pattern is valid, it can be re-revealed by using multiple similar network motifs (H2). Third, we hypothesize II. BACKGROUND that clusters derived using large network motifs are larger than the clusters derived using small network motifs (H3). Current studies on biological networks focus on local Our contributions are two-fold. First, for a given biological network properties, i.e. small-world [22], power-law degree network, it is usually unclear what are the representative distribution [23], network transitivity [24], network motif [1], network motifs that are most suitable for studying the local and community structure [25]. Among them, a widely-used higher-order organizations. Only using the existing network method for exploring higher-order network structures is to motifs identified from other networks will result in significant identify network motifs using graph mining techniques [8]. loss-of-information problem. Since the current network motif In this section, we introduce the network related concepts, discovery tools are computationally prohibitive, esp. for meso- followed by the existing methods for network motif discovery scale network motifs, we present a new method called FSM and the existing methods
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