On efficient network similarity measures

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On efficient network similarity measures. / Dehmer, Matthias; Chen, Zengqiang; Shi, Yongtang; Zhang, Y.; Tripathi, Shailesh; Ghorbani, Modjtaba; Mowshowitz, Abbe; Emmert-Streib, F.

In: Applied Mathematics and Computation, Vol. 362, 124521, 01.12.2019.

Research output: Contribution to journal › Article › Scientific › peer-review

Harvard

Dehmer, M, Chen, Z, Shi, Y, Zhang, Y, Tripathi, S, Ghorbani, M, Mowshowitz, A & Emmert-Streib, F 2019, 'On efficient network similarity measures', Applied Mathematics and Computation, vol. 362, 124521. https://doi.org/10.1016/j.amc.2019.06.035

APA

Dehmer, M., Chen, Z., Shi, Y., Zhang, Y., Tripathi, S., Ghorbani, M., ... Emmert-Streib, F. (2019). On efficient network similarity measures. Applied Mathematics and Computation, 362, [124521]. https://doi.org/10.1016/j.amc.2019.06.035

Vancouver

Dehmer M, Chen Z, Shi Y, Zhang Y, Tripathi S, Ghorbani M et al. On efficient network similarity measures. Applied Mathematics and Computation. 2019 Dec 1;362. 124521. https://doi.org/10.1016/j.amc.2019.06.035

Author

Dehmer, Matthias ; Chen, Zengqiang ; Shi, Yongtang ; Zhang, Y. ; Tripathi, Shailesh ; Ghorbani, Modjtaba ; Mowshowitz, Abbe ; Emmert-Streib, F. / On efficient network similarity measures. In: Applied Mathematics and Computation. 2019 ; Vol. 362.

Bibtex - Download

@article{1d690ed0ec5e4552a492d76cd445b997,

title = "On efficient network similarity measures",

abstract = "This paper presents novel graph similarity measures which can be applied to simple directed and undirected networks. To define the graph similarity measures, we first map graphs to real numbers by utilizing structural graph measures. Then, we define measures of similarity between real numbers and prove that they can be used as proxies for graph similarity. Numerical results are derived to show the domain coverage of these measures as well as their clustering ability. The latter relates to the efficient grouping of graphs according to certain structural properties. Our numerical results are sensitive to these properties and offer insights useful for designing effective graph similarity measures.",

keywords = "Distance measures, Graphs, Inequalities, Networks, Similarity measures",

author = "Matthias Dehmer and Zengqiang Chen and Yongtang Shi and Y. Zhang and Shailesh Tripathi and Modjtaba Ghorbani and Abbe Mowshowitz and F. Emmert-Streib",

year = "2019",

month = "12",

day = "1",

doi = "10.1016/j.amc.2019.06.035",

language = "English",

volume = "362",

journal = "Applied Mathematics and Computation",

issn = "0096-3003",

publisher = "Elsevier",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - On efficient network similarity measures

AU - Dehmer, Matthias

AU - Chen, Zengqiang

AU - Shi, Yongtang

AU - Zhang, Y.

AU - Tripathi, Shailesh

AU - Ghorbani, Modjtaba

AU - Mowshowitz, Abbe

AU - Emmert-Streib, F.

PY - 2019/12/1

Y1 - 2019/12/1

N2 - This paper presents novel graph similarity measures which can be applied to simple directed and undirected networks. To define the graph similarity measures, we first map graphs to real numbers by utilizing structural graph measures. Then, we define measures of similarity between real numbers and prove that they can be used as proxies for graph similarity. Numerical results are derived to show the domain coverage of these measures as well as their clustering ability. The latter relates to the efficient grouping of graphs according to certain structural properties. Our numerical results are sensitive to these properties and offer insights useful for designing effective graph similarity measures.

AB - This paper presents novel graph similarity measures which can be applied to simple directed and undirected networks. To define the graph similarity measures, we first map graphs to real numbers by utilizing structural graph measures. Then, we define measures of similarity between real numbers and prove that they can be used as proxies for graph similarity. Numerical results are derived to show the domain coverage of these measures as well as their clustering ability. The latter relates to the efficient grouping of graphs according to certain structural properties. Our numerical results are sensitive to these properties and offer insights useful for designing effective graph similarity measures.

KW - Distance measures

KW - Graphs

KW - Inequalities

KW - Networks

KW - Similarity measures

U2 - 10.1016/j.amc.2019.06.035

DO - 10.1016/j.amc.2019.06.035

M3 - Article

VL - 362

JO - Applied Mathematics and Computation

JF - Applied Mathematics and Computation

SN - 0096-3003

M1 - 124521

ER -

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