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Pregled bibliografske jedinice broj: 1127234

WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks


Fernandes, Sofia; Fanaee‑T, Hadi; Gama, João; Tišljarić, Leo; Šmuc, Tomislav
WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks // Machine learning (2021) doi:10.1007/s10994-021-05979-8 (znanstveni, online first)


CROSBI ID: 1127234 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks

Autori
Fernandes, Sofia ; Fanaee‑T, Hadi ; Gama, João ; Tišljarić, Leo ; Šmuc, Tomislav

Vrsta, podvrsta
Radovi u časopisima, znanstveni

Izvornik
Machine learning (2021)

Status rada
Online first

Ključne riječi
Time-evolving networks ; Tensor decomposition ; Event detection

Sažetak
Densification events in time-evolving networks refer to instants in which the network density, that is, the number of edges, is substantially larger than in the remaining. These events can occur at a global level, involving the majority of the nodes in the network, or at a local level involving only a subset of nodes. While global densification events affect the overall structure of the network, the same does not hold in local densification events, which may remain undetectable by the existing detection methods. In order to address this issue, we propose WINdowed TENsor decomposition for Densification Event Detection (WINTENDED) for the detection and characterization of both global and local densification events. Our method combines a sliding window decomposition with statistical tools to capture the local dynamics of the network and automatically find the irregular behaviours. According to our experimental evaluation, WINTENDED is able to spot global densification events at least as accurately as its competitors, while also being able to find local densification events, on the contrary to its competitors.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Tehnologija prometa i transport, Interdisciplinarne tehničke znanosti, Informacijske i komunikacijske znanosti, Interdisciplinarne društvene znanosti

Napomena
Part of a collection:
Special Issue on Discovery Science (2019)



POVEZANOST RADA


Projekti:
EK-KF-KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Lončarić, Sven; Ivan, Petrović, EK - KK.01.1.1.01) ( POIROT)

Ustanove:
Institut "Ruđer Bošković", Zagreb,
Fakultet prometnih znanosti, Zagreb

Profili:

Avatar Url Tomislav Šmuc (autor)

Avatar Url Leo Tišljarić (autor)

Citiraj ovu publikaciju

Fernandes, Sofia; Fanaee‑T, Hadi; Gama, João; Tišljarić, Leo; Šmuc, Tomislav
WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks // Machine learning (2021) doi:10.1007/s10994-021-05979-8 (znanstveni, online first)
Fernandes, S., Fanaee‑T, H., Gama, J., Tišljarić, L. & Šmuc, T. (2021) WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks. Prihvaćen za objavljivanje u Machine learning. [Preprint] doi:10.1007/s10994-021-05979-8.
@unknown{unknown, year = {2021}, DOI = {10.1007/s10994-021-05979-8}, keywords = {Time-evolving networks, Tensor decomposition, Event detection}, journal = {Machine learning}, doi = {10.1007/s10994-021-05979-8}, title = {WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks}, keyword = {Time-evolving networks, Tensor decomposition, Event detection} }
@unknown{unknown, year = {2021}, DOI = {10.1007/s10994-021-05979-8}, keywords = {Time-evolving networks, Tensor decomposition, Event detection}, journal = {Machine learning}, doi = {10.1007/s10994-021-05979-8}, title = {WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks}, keyword = {Time-evolving networks, Tensor decomposition, Event detection} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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