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
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Communication dans un congrès
21-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), Apr 2013, Bruges, Belgium. pp.113-118, 2013
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https://hal.archives-ouvertes.fr/hal-00825967
Contributeur : Fabrice Rossi <>
Soumis le : jeudi 17 octobre 2013 - 21:35:05
Dernière modification le : vendredi 18 octobre 2013 - 06:32:52

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  • HAL Id : hal-00825967, version 2
  • ARXIV : 1310.4914

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Fabrice Rossi, Pierre Latouche. Activity date estimation in timestamped interaction networks. 21-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), Apr 2013, Bruges, Belgium. pp.113-118, 2013. <hal-00825967v2>

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