Graph characterisation using graphlet-based entropies

Furqan Aziz; Mian Saeed  Akbar; Muhammad  Jawad; Abdul Haseeb  Malik; M. Irfan  Uddin; Georgios Gkoutos

doi:10.1016/j.patrec.2021.03.031

Graph characterisation using graphlet-based entropies

Furqan Aziz, Mian Saeed Akbar, Muhammad Jawad, Abdul Haseeb Malik, M. Irfan Uddin, Georgios Gkoutos

Cancer and Genomic Sciences

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Abstract

In this paper, we present a general framework to estimate the network entropy that is represented by means of an undirected graph and subsequently employ this framework for graph classification tasks. The proposed framework is based on local information functionals which are defined using induced connected subgraphs of different sizes. These induced subgraphs are termed graphlets. Specifically, we extract the set of all graphlets of a specific sizes and compute the graph entropy using our proposed framework. To classify the network into different categories, we construct a feature vector whose components are obtained by computing entropies of different graphlet sizes. We apply the proposed framework to two different tasks, namely view-based object recognition and biomedical datasets with binary outcomes classification. Finally, we report and compare the classification accuracies of the proposed method and compare against some of the state-of-the-art methods.

Original language	English
Pages (from-to)	100-107
Number of pages	8
Journal	Pattern Recognition Letters
Volume	147
Early online date	15 Apr 2021
DOIs	https://doi.org/10.1016/j.patrec.2021.03.031
Publication status	Published - Jul 2021

Bibliographical note

Funding Information:
GVG and FA acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032 ) and the NIHR Birmingham Biomedical Research Centre and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health.

Keywords

Graph characterisation
Graph entropy
Graphlets
Information functional

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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AzizF2021GraphAccepted author manuscript, 796 KBLicence: Creative Commons: Attribution-NonCommercial-NoDerivs (CC BY-NC-ND)

Cite this

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title = "Graph characterisation using graphlet-based entropies",

abstract = "In this paper, we present a general framework to estimate the network entropy that is represented by means of an undirected graph and subsequently employ this framework for graph classification tasks. The proposed framework is based on local information functionals which are defined using induced connected subgraphs of different sizes. These induced subgraphs are termed graphlets. Specifically, we extract the set of all graphlets of a specific sizes and compute the graph entropy using our proposed framework. To classify the network into different categories, we construct a feature vector whose components are obtained by computing entropies of different graphlet sizes. We apply the proposed framework to two different tasks, namely view-based object recognition and biomedical datasets with binary outcomes classification. Finally, we report and compare the classification accuracies of the proposed method and compare against some of the state-of-the-art methods.",

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author = "Furqan Aziz and Akbar, {Mian Saeed} and Muhammad Jawad and Malik, {Abdul Haseeb} and Uddin, {M. Irfan} and Georgios Gkoutos",

note = "Funding Information: GVG and FA acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032 ) and the NIHR Birmingham Biomedical Research Centre and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health.",

year = "2021",

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doi = "10.1016/j.patrec.2021.03.031",

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AU - Aziz, Furqan

AU - Akbar, Mian Saeed

AU - Jawad, Muhammad

AU - Malik, Abdul Haseeb

AU - Uddin, M. Irfan

AU - Gkoutos, Georgios

N1 - Funding Information: GVG and FA acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032 ) and the NIHR Birmingham Biomedical Research Centre and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health.

PY - 2021/7

Y1 - 2021/7

N2 - In this paper, we present a general framework to estimate the network entropy that is represented by means of an undirected graph and subsequently employ this framework for graph classification tasks. The proposed framework is based on local information functionals which are defined using induced connected subgraphs of different sizes. These induced subgraphs are termed graphlets. Specifically, we extract the set of all graphlets of a specific sizes and compute the graph entropy using our proposed framework. To classify the network into different categories, we construct a feature vector whose components are obtained by computing entropies of different graphlet sizes. We apply the proposed framework to two different tasks, namely view-based object recognition and biomedical datasets with binary outcomes classification. Finally, we report and compare the classification accuracies of the proposed method and compare against some of the state-of-the-art methods.

AB - In this paper, we present a general framework to estimate the network entropy that is represented by means of an undirected graph and subsequently employ this framework for graph classification tasks. The proposed framework is based on local information functionals which are defined using induced connected subgraphs of different sizes. These induced subgraphs are termed graphlets. Specifically, we extract the set of all graphlets of a specific sizes and compute the graph entropy using our proposed framework. To classify the network into different categories, we construct a feature vector whose components are obtained by computing entropies of different graphlet sizes. We apply the proposed framework to two different tasks, namely view-based object recognition and biomedical datasets with binary outcomes classification. Finally, we report and compare the classification accuracies of the proposed method and compare against some of the state-of-the-art methods.

KW - Graph characterisation

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KW - Information functional

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JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

ER -

Graph characterisation using graphlet-based entropies

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this