Activity date estimation in timestamped interaction networks

Fabrice Rossi; Pierre Latouche

Activity date estimation in timestamped interaction networks

Fabrice Rossi
, Pierre Latouche

Université Paris 1 Panthéon-Sorbonne

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

Abstract

We propose in this paper a new generative model for graphs that uses a latent space approach to explain timestamped interactions. The model is designed to provide global estimates of activity dates in historical networks where only the interaction dates between agents are known with reasonable precision. Experimental results show that the model provides better results than local averages in dense enough networks.

Original language	English
Title of host publication	ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisher	i6doc.com publication
Pages	113-118
Number of pages	6
ISBN (Print)	9782874190810
Publication status	Published - 1 Jan 2013
Externally published	Yes
Event	21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 - Bruges, Belgium Duration: 24 Apr 2013 → 26 Apr 2013

Publication series

Name	ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference	21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013
Country/Territory	Belgium
City	Bruges
Period	24/04/13 → 26/04/13

Cite this

Rossi, F., & Latouche, P. (2013). Activity date estimation in timestamped interaction networks. In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 113-118). (ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). i6doc.com publication.

Rossi, Fabrice ; Latouche, Pierre. / Activity date estimation in timestamped interaction networks. ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2013. pp. 113-118 (ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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title = "Activity date estimation in timestamped interaction networks",

abstract = "We propose in this paper a new generative model for graphs that uses a latent space approach to explain timestamped interactions. The model is designed to provide global estimates of activity dates in historical networks where only the interaction dates between agents are known with reasonable precision. Experimental results show that the model provides better results than local averages in dense enough networks.",

author = "Fabrice Rossi and Pierre Latouche",

year = "2013",

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language = "English",

isbn = "9782874190810",

series = "ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

publisher = "i6doc.com publication",

pages = "113--118",

booktitle = "ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

note = "21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 ; Conference date: 24-04-2013 Through 26-04-2013",

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Rossi, F & Latouche, P 2013, Activity date estimation in timestamped interaction networks. in ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, i6doc.com publication, pp. 113-118, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013, Bruges, Belgium, 24/04/13.

Activity date estimation in timestamped interaction networks. / Rossi, Fabrice; Latouche, Pierre.
ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2013. p. 113-118 (ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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

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T1 - Activity date estimation in timestamped interaction networks

AU - Rossi, Fabrice

AU - Latouche, Pierre

PY - 2013/1/1

Y1 - 2013/1/1

N2 - We propose in this paper a new generative model for graphs that uses a latent space approach to explain timestamped interactions. The model is designed to provide global estimates of activity dates in historical networks where only the interaction dates between agents are known with reasonable precision. Experimental results show that the model provides better results than local averages in dense enough networks.

AB - We propose in this paper a new generative model for graphs that uses a latent space approach to explain timestamped interactions. The model is designed to provide global estimates of activity dates in historical networks where only the interaction dates between agents are known with reasonable precision. Experimental results show that the model provides better results than local averages in dense enough networks.

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M3 - Conference contribution

AN - SCOPUS:84887037297

SN - 9782874190810

T3 - ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

SP - 113

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BT - ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

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Y2 - 24 April 2013 through 26 April 2013

ER -

Rossi F, Latouche P. Activity date estimation in timestamped interaction networks. In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication. 2013. p. 113-118. (ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Activity date estimation in timestamped interaction networks

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