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Topological Data Analysis Approach for Weighted Networks Embedding. / Knyazeva, Irina; Talalaeva, Olga.

Networks in the Global World V - Proceedings of NetGloW 2020. ed. / Artem Antonyuk; Nikita Basov. Springer Nature, 2021. p. 81-100 (Lecture Notes in Networks and Systems; Vol. 181).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Knyazeva, I & Talalaeva, O 2021, Topological Data Analysis Approach for Weighted Networks Embedding. in A Antonyuk & N Basov (eds), Networks in the Global World V - Proceedings of NetGloW 2020. Lecture Notes in Networks and Systems, vol. 181, Springer Nature, pp. 81-100, 5th Networks in the Global World Conference, NetGloW 2020, St. Petersburg, Russian Federation, 7/07/20. https://doi.org/10.1007/978-3-030-64877-0_6

APA

Knyazeva, I., & Talalaeva, O. (2021). Topological Data Analysis Approach for Weighted Networks Embedding. In A. Antonyuk, & N. Basov (Eds.), Networks in the Global World V - Proceedings of NetGloW 2020 (pp. 81-100). (Lecture Notes in Networks and Systems; Vol. 181). Springer Nature. https://doi.org/10.1007/978-3-030-64877-0_6

Vancouver

Knyazeva I, Talalaeva O. Topological Data Analysis Approach for Weighted Networks Embedding. In Antonyuk A, Basov N, editors, Networks in the Global World V - Proceedings of NetGloW 2020. Springer Nature. 2021. p. 81-100. (Lecture Notes in Networks and Systems). https://doi.org/10.1007/978-3-030-64877-0_6

Author

Knyazeva, Irina ; Talalaeva, Olga. / Topological Data Analysis Approach for Weighted Networks Embedding. Networks in the Global World V - Proceedings of NetGloW 2020. editor / Artem Antonyuk ; Nikita Basov. Springer Nature, 2021. pp. 81-100 (Lecture Notes in Networks and Systems).

BibTeX

@inproceedings{3104adba5bae439b820df0a1f7a444b3,
title = "Topological Data Analysis Approach for Weighted Networks Embedding",
abstract = "Efficient node representation for weighted networks is an important problem for many domains in real-world network analysis. Network exploration usually comes down to description of some structural features which give information about network properties in general, but not about specific nodes. Whereas information about node profile is very important in any network with attributed nodes. Recently, the network embedding approach has emerged, which could be formalized as mapping each node in the undirected and weighted graph into a d-dimensional vector that captures its structural properties. The output representation can be used as the input for a variety of data analysis tasks as well as for individual node analysis. We suggested using an additional approach for node description based on the topological structure of the network. Some interesting topological features of such data can be revealed with topological data analysis. Each node in this case may be described in terms of participation in topological cavities of different sizes and their persistence. Such representation was used as an alternative node description and demonstrated their efficiency in the community detection task. In order to test the approach, we used a weighted stochastic block model with different parameters as a network generative process.",
keywords = "Node embedding, Topological data analysis, Weighted networks, WSBM",
author = "Irina Knyazeva and Olga Talalaeva",
note = "Publisher Copyright: {\textcopyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.; 5th Networks in the Global World Conference, NetGloW 2020 ; Conference date: 07-07-2020 Through 09-07-2020",
year = "2021",
doi = "10.1007/978-3-030-64877-0_6",
language = "English",
isbn = "9783030648763",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Nature",
pages = "81--100",
editor = "Artem Antonyuk and Nikita Basov",
booktitle = "Networks in the Global World V - Proceedings of NetGloW 2020",
address = "Germany",
url = "http://ngw.spbu.ru/",

}

RIS

TY - GEN

T1 - Topological Data Analysis Approach for Weighted Networks Embedding

AU - Knyazeva, Irina

AU - Talalaeva, Olga

N1 - Publisher Copyright: © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2021

Y1 - 2021

N2 - Efficient node representation for weighted networks is an important problem for many domains in real-world network analysis. Network exploration usually comes down to description of some structural features which give information about network properties in general, but not about specific nodes. Whereas information about node profile is very important in any network with attributed nodes. Recently, the network embedding approach has emerged, which could be formalized as mapping each node in the undirected and weighted graph into a d-dimensional vector that captures its structural properties. The output representation can be used as the input for a variety of data analysis tasks as well as for individual node analysis. We suggested using an additional approach for node description based on the topological structure of the network. Some interesting topological features of such data can be revealed with topological data analysis. Each node in this case may be described in terms of participation in topological cavities of different sizes and their persistence. Such representation was used as an alternative node description and demonstrated their efficiency in the community detection task. In order to test the approach, we used a weighted stochastic block model with different parameters as a network generative process.

AB - Efficient node representation for weighted networks is an important problem for many domains in real-world network analysis. Network exploration usually comes down to description of some structural features which give information about network properties in general, but not about specific nodes. Whereas information about node profile is very important in any network with attributed nodes. Recently, the network embedding approach has emerged, which could be formalized as mapping each node in the undirected and weighted graph into a d-dimensional vector that captures its structural properties. The output representation can be used as the input for a variety of data analysis tasks as well as for individual node analysis. We suggested using an additional approach for node description based on the topological structure of the network. Some interesting topological features of such data can be revealed with topological data analysis. Each node in this case may be described in terms of participation in topological cavities of different sizes and their persistence. Such representation was used as an alternative node description and demonstrated their efficiency in the community detection task. In order to test the approach, we used a weighted stochastic block model with different parameters as a network generative process.

KW - Node embedding

KW - Topological data analysis

KW - Weighted networks

KW - WSBM

UR - http://www.scopus.com/inward/record.url?scp=85102620102&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-64877-0_6

DO - 10.1007/978-3-030-64877-0_6

M3 - Conference contribution

AN - SCOPUS:85102620102

SN - 9783030648763

T3 - Lecture Notes in Networks and Systems

SP - 81

EP - 100

BT - Networks in the Global World V - Proceedings of NetGloW 2020

A2 - Antonyuk, Artem

A2 - Basov, Nikita

PB - Springer Nature

T2 - 5th Networks in the Global World Conference, NetGloW 2020

Y2 - 7 July 2020 through 9 July 2020

ER -

ID: 92451160