TimeRank: A random walk approach for community discovery in dynamic networks

Ilias Sarantopoulos; Dimitrios Papatheodorou; Dimitrios Vogiatzis; Grigorios Tzortzis; Georgios Paliouras

doi:10.1007/978-3-030-05411-3_28

TimeRank: A random walk approach for community discovery in dynamic networks

Ilias Sarantopoulos^*, Dimitrios Papatheodorou, Dimitrios Vogiatzis, Grigorios Tzortzis, Georgios Paliouras

^*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

6 Sitaatiot (Scopus)

Abstrakti

In this work we consider the problem of discovering communities in time evolving social networks. We propose TimeRank, an algorithm for dynamic networks, which uses random walks on a tensor representation to detect time-evolving communities. The proposed algorithm is based on an earlier work on community detection in multi-relational networks. Detection of dynamic communities can be be done in two steps (segmentation of the network into time frames, detection of communities per time frame and tracking of communities across time frames). Alternatively it can be done in one step. TimeRank is a one step approach. We compared TimeRank with Non-Negative Tensor Factorisation and Group Evolution Discovery method on synthetic and real world data sets from Reddit.

Alkuperäiskieli	Englanti
Otsikko	Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018
Toimittajat	Renaud Lambiotte, Luis M. Rocha, Pietro Lió, Hocine Cherifi, Luca Maria Aiello, Chantal Cherifi
Kustantaja	SPRINGER
Sivut	338-350
Sivumäärä	13
ISBN (painettu)	9783030054106
DOI - pysyväislinkit	https://doi.org/10.1007/978-3-030-05411-3_28
Tila	Julkaistu - 1 tammik. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	International Conference on Complex Networks and their Applications - Cambridge, Iso-Britannia Kesto: 11 jouluk. 2018 → 13 jouluk. 2018 Konferenssinumero: 7 https://www.complexnetworks.org/

Julkaisusarja

Nimi	Studies in Computational Intelligence
Kustantaja	Springer
Vuosikerta	812
ISSN (painettu)	1860-949X

Conference

Conference	International Conference on Complex Networks and their Applications
Lyhennettä	COMPLEX NETWORKS
Maa/Alue	Iso-Britannia
Kaupunki	Cambridge
Ajanjakso	11/12/2018 → 13/12/2018
www-osoite	https://www.complexnetworks.org/

Pääsy asiakirjaan

10.1007/978-3-030-05411-3_28

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Sarantopoulos, I., Papatheodorou, D., Vogiatzis, D., Tzortzis, G., & Paliouras, G. (2019). TimeRank: A random walk approach for community discovery in dynamic networks. teoksessa R. Lambiotte, L. M. Rocha, P. Lió, H. Cherifi, L. M. Aiello, & C. Cherifi (Toimittajat), Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018 (Sivut 338-350). (Studies in Computational Intelligence; Vuosikerta 812). SPRINGER. https://doi.org/10.1007/978-3-030-05411-3_28

Sarantopoulos, Ilias ; Papatheodorou, Dimitrios ; Vogiatzis, Dimitrios et al. / TimeRank : A random walk approach for community discovery in dynamic networks. Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. Toimittaja / Renaud Lambiotte ; Luis M. Rocha ; Pietro Lió ; Hocine Cherifi ; Luca Maria Aiello ; Chantal Cherifi. SPRINGER, 2019. Sivut 338-350 (Studies in Computational Intelligence).

@inproceedings{bad6ae9abd34424b8b4bb988b105f105,

title = "TimeRank: A random walk approach for community discovery in dynamic networks",

abstract = "In this work we consider the problem of discovering communities in time evolving social networks. We propose TimeRank, an algorithm for dynamic networks, which uses random walks on a tensor representation to detect time-evolving communities. The proposed algorithm is based on an earlier work on community detection in multi-relational networks. Detection of dynamic communities can be be done in two steps (segmentation of the network into time frames, detection of communities per time frame and tracking of communities across time frames). Alternatively it can be done in one step. TimeRank is a one step approach. We compared TimeRank with Non-Negative Tensor Factorisation and Group Evolution Discovery method on synthetic and real world data sets from Reddit.",

author = "Ilias Sarantopoulos and Dimitrios Papatheodorou and Dimitrios Vogiatzis and Grigorios Tzortzis and Georgios Paliouras",

year = "2019",

month = jan,

day = "1",

doi = "10.1007/978-3-030-05411-3_28",

language = "English",

isbn = "9783030054106",

series = "Studies in Computational Intelligence",

publisher = "SPRINGER",

pages = "338--350",

editor = "Renaud Lambiotte and Rocha, {Luis M.} and Pietro Li{\'o} and Hocine Cherifi and Aiello, {Luca Maria} and Chantal Cherifi",

booktitle = "Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018",

address = "Germany",

note = "International Conference on Complex Networks and their Applications, COMPLEX NETWORKS ; Conference date: 11-12-2018 Through 13-12-2018",

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Sarantopoulos, I, Papatheodorou, D, Vogiatzis, D, Tzortzis, G & Paliouras, G 2019, TimeRank: A random walk approach for community discovery in dynamic networks. julkaisussa R Lambiotte, LM Rocha, P Lió, H Cherifi, LM Aiello & C Cherifi (toim), Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. Studies in Computational Intelligence, Vuosikerta 812, SPRINGER, Sivut 338-350, International Conference on Complex Networks and their Applications, Cambridge, Iso-Britannia, 11/12/2018. https://doi.org/10.1007/978-3-030-05411-3_28

TimeRank: A random walk approach for community discovery in dynamic networks. / Sarantopoulos, Ilias; Papatheodorou, Dimitrios; Vogiatzis, Dimitrios et al.
Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. toim. / Renaud Lambiotte; Luis M. Rocha; Pietro Lió; Hocine Cherifi; Luca Maria Aiello; Chantal Cherifi. SPRINGER, 2019. s. 338-350 (Studies in Computational Intelligence; Vuosikerta 812).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

TY - GEN

T1 - TimeRank

T2 - International Conference on Complex Networks and their Applications

AU - Sarantopoulos, Ilias

AU - Papatheodorou, Dimitrios

AU - Vogiatzis, Dimitrios

AU - Tzortzis, Grigorios

AU - Paliouras, Georgios

N1 - Conference code: 7

PY - 2019/1/1

Y1 - 2019/1/1

N2 - In this work we consider the problem of discovering communities in time evolving social networks. We propose TimeRank, an algorithm for dynamic networks, which uses random walks on a tensor representation to detect time-evolving communities. The proposed algorithm is based on an earlier work on community detection in multi-relational networks. Detection of dynamic communities can be be done in two steps (segmentation of the network into time frames, detection of communities per time frame and tracking of communities across time frames). Alternatively it can be done in one step. TimeRank is a one step approach. We compared TimeRank with Non-Negative Tensor Factorisation and Group Evolution Discovery method on synthetic and real world data sets from Reddit.

AB - In this work we consider the problem of discovering communities in time evolving social networks. We propose TimeRank, an algorithm for dynamic networks, which uses random walks on a tensor representation to detect time-evolving communities. The proposed algorithm is based on an earlier work on community detection in multi-relational networks. Detection of dynamic communities can be be done in two steps (segmentation of the network into time frames, detection of communities per time frame and tracking of communities across time frames). Alternatively it can be done in one step. TimeRank is a one step approach. We compared TimeRank with Non-Negative Tensor Factorisation and Group Evolution Discovery method on synthetic and real world data sets from Reddit.

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DO - 10.1007/978-3-030-05411-3_28

M3 - Conference contribution

AN - SCOPUS:85059054404

SN - 9783030054106

T3 - Studies in Computational Intelligence

SP - 338

EP - 350

BT - Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018

A2 - Lambiotte, Renaud

A2 - Rocha, Luis M.

A2 - Lió, Pietro

A2 - Cherifi, Hocine

A2 - Aiello, Luca Maria

A2 - Cherifi, Chantal

PB - SPRINGER

Y2 - 11 December 2018 through 13 December 2018

ER -

Sarantopoulos I, Papatheodorou D, Vogiatzis D, Tzortzis G, Paliouras G. TimeRank: A random walk approach for community discovery in dynamic networks. julkaisussa Lambiotte R, Rocha LM, Lió P, Cherifi H, Aiello LM, Cherifi C, toimittajat, Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. SPRINGER. 2019. s. 338-350. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-05411-3_28

TimeRank: A random walk approach for community discovery in dynamic networks

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