Motif discovery in networks: A survey

Yu, Shuo; Feng, Yufan; Zhang, Da; Bedru, Hayat Dino; Xu, Bo; Xia, Feng

doi:10.1016/j.cosrev.2020.100267

Cited by 42 publications

(27 citation statements)

References 33 publications

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“…In very recent work [13], authors define a new method to explain a network motif using the graph compression technique. They explain a sub-graph M considered as a network motif if the probability of M in G is greater than the probability of M in a null model of G. In [29] and [39], the authors review the different tools for finding network motifs in a network. Several papers like [38] and [33] propose clustering techniques using network motifs.…”

Section: Related Workmentioning

confidence: 99%

Impact of second-order network motif on online social networks

Sinha¹,

Bhattacharya²,

Roy³

2021

J Supercomput

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The behaviour of individual users in an online social network is a major contributing factor in determining the outcome of multiple network phenomenon. Group formation, growth of the network, information propagation, and rumour blocking are some of the many network behavioural traits that are influenced by the interaction patterns of the users in the network. Network motifs capture one such interaction pattern between users in online social networks (OSNs). For this work, four second-order (two-edged) network motifs have been considered, namely, message receiving pattern, message broadcasting pattern, message passing pattern, and reciprocal message pattern, to analyse user behaviour in online social networks. This work provides and utilizes a node interaction pattern-finding algorithm to identify the frequency of aforementioned second-order network motifs in six real-life online social networks (Facebook, GPlus, GNU, Twitter, Enron Email, and Wiki-vote). The frequency of network motifs participated in by a node is considered for the relative ranking of all nodes in the online social networks. The highest-rated nodes are considered seeds for information propagation. The performance of using network motifs for ranking nodes as seeds for information propagation is validated using statistical metrics Z-score, concentration, and significance profile and compared with baseline ranking methods in-degree centrality, out-degree centrality, closeness centrality, and PageRank. The comparative study shows the performance of centrality measures to be similar or better than second-order network motifs as seed nodes in information diffusion. The experimental results on finding frequencies and importance of different interaction patterns provide insights on the significance and representation of each such interaction pattern and how it varies from network to network.

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Section: Related Workmentioning

confidence: 99%

Impact of second-order network motif on online social networks

Sinha¹,

Bhattacharya²,

Roy³

2021

J Supercomput

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“…Recent survey of network motifs showed that there are different types of network motifs [18]. Originally, Milo et al [19] introduced network motifs as the "patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks" in 2002.…”

Section: Network Motifmentioning

confidence: 99%

“…There are various tools that follow network-centric or motif-centric approaches. After careful review of the tools [18,38,39], we found out that most tools are out-dated, dependent on operating systems or other external tools, restricted output options, inaccessible, or lack of usability. As shown in Table 3, we tested the following tools such as mFinder, FANMOD, Kavosh, QuateXelero, Fascia, ISMAGS, MotifNet, and Motif-Discovery, because others are unable to locate, unable to install, out-dated, incompatible with current security setting, or inexecutable.…”

Section: Toolsmentioning

confidence: 99%

NemoSuite: Web-based Network Motif Analytic Suite

Kim¹,

Hsu²,

Li³

et al. 2020

Adv. sci. technol. eng. syst. j.

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“…Previous studies have provided several approaches to extract network motifs from biological networks [ 8 – 11 ]. While these methods are useful for studying specific instances of network structures, it remains challenging to extract families of network motifs which contain diverse appearances of networks but produce unifying characteristics of dynamical systems.…”

Section: Introductionmentioning

confidence: 99%

Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks

Nordick

Hong

2021

BMC Bioinformatics

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Background Feedback loops in gene regulatory networks play pivotal roles in governing functional dynamics of cells. Systems approaches demonstrated characteristic dynamical features, including multistability and oscillation, of positive and negative feedback loops. Recent experiments and theories have implicated highly interconnected feedback loops (high-feedback loops) in additional nonintuitive functions, such as controlling cell differentiation rate and multistep cell lineage progression. However, it remains challenging to identify and visualize high-feedback loops in complex gene regulatory networks due to the myriad of ways in which the loops can be combined. Furthermore, it is unclear whether the high-feedback loop structures with these potential functions are widespread in biological systems. Finally, it remains challenging to understand diverse dynamical features, such as high-order multistability and oscillation, generated by individual networks containing high-feedback loops. To address these problems, we developed HiLoop, a toolkit that enables discovery, visualization, and analysis of several types of high-feedback loops in large biological networks. Results HiLoop not only extracts high-feedback structures and visualize them in intuitive ways, but also quantifies the enrichment of overrepresented structures. Through random parameterization of mathematical models derived from target networks, HiLoop presents characteristic features of the underlying systems, including complex multistability and oscillations, in a unifying framework. Using HiLoop, we were able to analyze realistic gene regulatory networks containing dozens to hundreds of genes, and to identify many small high-feedback systems. We found more than a 100 human transcription factors involved in high-feedback loops that were not studied previously. In addition, HiLoop enabled the discovery of an enrichment of high feedback in pathways related to epithelial-mesenchymal transition. Conclusions HiLoop makes the study of complex networks accessible without significant computational demands. It can serve as a hypothesis generator through identification and modeling of high-feedback subnetworks, or as a quantification method for motif enrichment analysis. As an example of discovery, we found that multistep cell lineage progression may be driven by either specific instances of high-feedback loops with sparse appearances, or generally enriched topologies in gene regulatory networks. We expect HiLoop’s usefulness to increase as experimental data of regulatory networks accumulate. Code is freely available for use or extension at https://github.com/BenNordick/HiLoop.

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Motif discovery in networks: A survey

Cited by 42 publications

References 33 publications

Impact of second-order network motif on online social networks

Impact of second-order network motif on online social networks

NemoSuite: Web-based Network Motif Analytic Suite

Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks

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