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Counting motifs in dynamic networks

Overview of attention for article published in BMC Systems Biology, April 2018
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Title
Counting motifs in dynamic networks
Published in
BMC Systems Biology, April 2018
DOI 10.1186/s12918-018-0533-6
Pubmed ID
Authors

Kingshuk Mukherjee, Md Mahmudul Hasan, Christina Boucher, Tamer Kahveci

Abstract

A network motif is a sub-network that occurs frequently in a given network. Detection of such motifs is important since they uncover functions and local properties of the given biological network. Finding motifs is however a computationally challenging task as it requires solving the costly subgraph isomorphism problem. Moreover, the topology of biological networks change over time. These changing networks are called dynamic biological networks. As the network evolves, frequency of each motif in the network also changes. Computing the frequency of a given motif from scratch in a dynamic network as the network topology evolves is infeasible, particularly for large and fast evolving networks. In this article, we design and develop a scalable method for counting the number of motifs in a dynamic biological network. Our method incrementally updates the frequency of each motif as the underlying network's topology evolves. Our experiments demonstrate that our method can update the frequency of each motif in orders of magnitude faster than counting the motif embeddings every time the network changes. If the network evolves more frequently, the margin with which our method outperforms the existing static methods, increases. We evaluated our method extensively using synthetic and real datasets, and show that our method is highly accurate(≥ 96%) and that it can be scaled to large dense networks. The results on real data demonstrate the utility of our method in revealing interesting insights on the evolution of biological processes.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 25%
Professor 2 10%
Researcher 2 10%
Professor > Associate Professor 2 10%
Student > Master 2 10%
Other 4 20%
Unknown 3 15%
Readers by discipline Count As %
Computer Science 7 35%
Agricultural and Biological Sciences 4 20%
Biochemistry, Genetics and Molecular Biology 2 10%
Psychology 1 5%
Physics and Astronomy 1 5%
Other 2 10%
Unknown 3 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 April 2018.
All research outputs
#11,384,138
of 12,799,521 outputs
Outputs from BMC Systems Biology
#932
of 1,071 outputs
Outputs of similar age
#239,439
of 274,397 outputs
Outputs of similar age from BMC Systems Biology
#20
of 29 outputs
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