Skip to main content

2019 | OriginalPaper | Buchkapitel

Link Prediction Based on Time Series of Similarity Coefficients and Structural Function

verfasst von : Piotr Stąpor, Ryszard Antkiewicz, Mariusz Chmielewski

Erschienen in: Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

A social network is a structure whose nodes represent people or other entities embedded in a social context while its edges symbolize interaction, collaboration or exertion of influence between these fore-mentioned entities [3]. From a wide class of problems related to social networks, the ones related to link dynamics seems particularly interesting. A noteworthy link prediction technique, based on analyzing the history of the network (i.e. its previous states), was presented by Prudêncio and da Silva Soares in [5]. In this paper, we attempt to improve the quality of edges’ formation prognosis in social networks by proposing a modified version of aforementioned method. For that purpose we shall compute values of certain similarity coefficients and use them as an input to a supervised classification mechanism (called structural function). We stipulate that this function changes over time, thus making it possible to derive time series for all of its parameters and obtain their next values using a forecasting model. We might then predict new links’ occurrences using the forecasted values of similarity metrics and supervised classification method with the predicted parameters. This paper contains also the comparison of ROC charts for both legacy solution and the novel method.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Fußnoten
1
The authors cite 131 papers in their publication.
 
2
Similarity coefficients’ values are random variables as they are functions of a random graph structure.
 
Literatur
1.
Zurück zum Zitat Barthélemy, M., Chow, E., Eliassi-rad, T.: Knowledge representation issues in semantic graphs for relationship detection. In: AAAI Spring Symposium on AI Technologies for Homeland Security, pp. 91–98. AAAI Press (2005) Barthélemy, M., Chow, E., Eliassi-rad, T.: Knowledge representation issues in semantic graphs for relationship detection. In: AAAI Spring Symposium on AI Technologies for Homeland Security, pp. 91–98. AAAI Press (2005)
3.
Zurück zum Zitat Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRef Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRef
4.
Zurück zum Zitat Lu, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A: Stat. Mech. Appl. 390(6), 1150–1170 (2010)CrossRef Lu, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A: Stat. Mech. Appl. 390(6), 1150–1170 (2010)CrossRef
5.
Zurück zum Zitat da Silva Soares, P.R., Prudêncio, R.B.: Time series based link prediction. In: Proceedings of the International Joint Conference on Neural Networks, June 2012 da Silva Soares, P.R., Prudêncio, R.B.: Time series based link prediction. In: Proceedings of the International Joint Conference on Neural Networks, June 2012
6.
Zurück zum Zitat Rossetti, G., Guidotti, R., Miliou, I., Pedreschi, D., Giannotti, F.: A supervised approach for intra-/inter-community interaction prediction in dynamic social networks. Soc. Netw. Anal. Min. 6(1), 86 (2016)CrossRef Rossetti, G., Guidotti, R., Miliou, I., Pedreschi, D., Giannotti, F.: A supervised approach for intra-/inter-community interaction prediction in dynamic social networks. Soc. Netw. Anal. Min. 6(1), 86 (2016)CrossRef
8.
Zurück zum Zitat Zeliaś, A., Pawełek, B., Wanat, S.: Prognozowanie ekonomiczne: teoria, przykłady, zadania. Wydawnictwo Naukowe PWN (2003) Zeliaś, A., Pawełek, B., Wanat, S.: Prognozowanie ekonomiczne: teoria, przykłady, zadania. Wydawnictwo Naukowe PWN (2003)
Metadaten
Titel
Link Prediction Based on Time Series of Similarity Coefficients and Structural Function
verfasst von
Piotr Stąpor
Ryszard Antkiewicz
Mariusz Chmielewski
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-19093-4_13