Exploiting graphlet decomposition to explain the structure of complex networks: the GHuST framework
| Autor: | Guillermo Mestre, Tao Huang, Andres Ramos, Fernando Postigo, Rafael Espejo, Sara Lumbreras, Ettore Francesco Bompard |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Structure (mathematical logic) Multidisciplinary Theoretical computer science Computer science Energy science and technology Physics lcsh:R lcsh:Medicine 02 engineering and technology Complex network Network topology Article 03 medical and health sciences 030104 developmental biology 020204 information systems Principal component analysis 0202 electrical engineering electronic engineering information engineering Decomposition (computer science) lcsh:Q lcsh:Science Representation (mathematics) Topology (chemistry) Independence (probability theory) |
| Zdroj: | Scientific Reports, Vol 10, Iss 1, Pp 1-14 (2020) Scientific Reports |
| Popis: | The characterization of topology is crucial in understanding network evolution and behavior. This paper presents an innovative approach, the GHuST framework to describe complex-network topology from graphlet decomposition. This new framework exploits the local information provided by graphlets to give a global explanation of network topology. The GHuST framework is comprised of 12 metrics that analyze how 2- and 3-node graphlets shape the structure of networks. The main strengths of the GHuST framework are enhanced topological description, size independence, and computational simplicity. It allows for straight comparison among different networks disregarding their size. It also reduces the complexity of graphlet counting, since it does not use 4- and 5-node graphlets. The application of the novel framework to a large set of networks shows that it can classify networks of distinct nature based on their topological properties. To ease network classification and enhance the graphical representation of them, we reduce the 12 dimensions to their main principal components. Furthermore, the 12 dimensions are easily interpretable. This enables the connection between complex-network analyses and diverse real applications. |
| Databáze: | OpenAIRE |
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