Review of tools and algorithms for network motif discovery in biological networks

Patra, Sabyasachi; Mohapatra, Anjali

doi:10.1049/iet-syb.2020.0004

Cited by 19 publications

(8 citation statements)

References 77 publications

(226 reference statements)

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“…Motives are similar in meaning to graphlets but are used for the statistical analysis of large-scale networks, including the identification of properties and characteristics [48][49][50][51]. The types of motives and the number of their occurrences in the graph are calculated.…”

Section: Resultsmentioning

confidence: 99%

Introduction to the Class of Prefractal Graphs

Kochkarov

2022

Mathematics

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Fractals are already firmly rooted in modern science. Research continues on the fractal properties of objects in physics, chemistry, biology and many other scientific fields. Fractal graphs as a discrete representation are used to model and describe the structure of various objects and processes, both natural and artificial. The paper proposes an introduction to prefractal graphs. The main definitions and notation are proposed—the concept of a seed, the operations of processing a seed, the procedure for generating a prefractal graph. Canonical (typical) and non-canonical (special) types of prefractal graphs are considered separately. Important characteristics are proposed and described—the preservation of adjacency of edges for different ranks in the trajectory. The definition of subgraph-seeds of different ranks is given separately. Rules for weighting a prefractal graph by natural numbers and intervals are proposed. Separately, the definition of a fractal graph as infinite is given, and the differences between the concepts of fractal and prefractal graphs are described. At the end of the work, already published works of the authors are proposed, indicating the main backlogs, as well as a list of directions for new research. This work is the beginning of a cycle of works on the study of the properties and characteristics of fractal and prefractal graphs.

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Section: Resultsmentioning

confidence: 99%

Introduction to the Class of Prefractal Graphs

Kochkarov

2022

Mathematics

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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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“…This work measured and compared motifs across contexts, including gene regulatory networks (building on [63]), neuronal networks, food webs, electronic circuits, and the World Wide Web, using network randomization to define a notion of significance for each motif. Much of the early work on motifs in biological systems has been reviewed in [3] and more recently in [46]. The null models that underly motif comparisons are typically samples from ensembles of random graphs that preserve properties relevant to the particular networks under study [27].…”

Section: Related Workmentioning

confidence: 99%

Sequential Motifs in Observed Walks

LaRock¹,

Scholtes²,

Eliassi-Rad³

2021

Preprint

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The structure of complex networks can be characterized by counting and analyzing network motifs. Motifs are small subgraphs that occur repeatedly in a network, such as triangles or chains. Recent work has generalized motifs to temporal and dynamic network data. However, existing techniques do not generalize to sequential or trajectory data, which represents entities moving through the nodes of a network, such as passengers moving through transportation networks. The unit of observation in these data is fundamentally different, since we analyze full observations of trajectories (e.g., a trip from airport A to airport C through airport B), rather than independent observations of edges or snapshots of graphs over time. In this work, we define sequential motifs in trajectory data, which are small, directed, and edge-weighted subgraphs corresponding to patterns in observed sequences. We draw a connection between counting and analysis of sequential motifs and Higher-Order Network (HON) models. We show that by mapping edges of a HON, specifically a kthorder DeBruijn graph, to sequential motifs, we can count and evaluate their importance in observed data. We test our methodology with two datasets: (1) passengers navigating an airport network and (2) people navigating the Wikipedia article network. We find that the most prevalent and important sequential motifs correspond to intuitive patterns of traversal in the real systems, and show empirically that the heterogeneity of edge weights in an observed higher-order DeBruijn graph has implications for the distributions of sequential motifs we expect to see across our null models.

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Review of tools and algorithms for network motif discovery in biological networks

Cited by 19 publications

References 77 publications

Introduction to the Class of Prefractal Graphs

Introduction to the Class of Prefractal Graphs

Impact of second-order network motif on online social networks

Sequential Motifs in Observed Walks

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