Model-free inference of diffusion networks using RKHS embeddings

• Hu, Shoubo ^[1] ; Cautis, Bogdan ^[2] ; Chen, Zhitang ^[3] ; Chan, Laiwan ^[1] ; Geng, Yanhui ^[3] ; He, Xiuqiang ^[3]
  1. [1] Chinese University of Hong Kong

     Chinese University of Hong Kong

     RAE de Hong Kong (China)

     
  2. [2] University of Paris-Sud

     University of Paris-Sud

     Arrondissement de Palaiseau, Francia

     
  3. [3] Huawei Noah’s Ark Lab

  Mostrar afiliaciones +
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 33, Nº 2, 2019, págs. 499-525
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • We revisit in this paper the problem of inferring a diffusion network from information cascades. In our study, we make no assumptions on the underlying diffusion model, in this way obtaining a generic method with broader practical applicability. Our approach exploits the pairwise adoption-time intervals from cascades. Starting from the observation that different kinds of information spread differently, these time intervals are interpreted as samples drawn from unknown (conditional) distributions. In order to statistically distinguish them, we propose a novel method using Reproducing Kernel Hilbert Space embeddings. Experiments on both synthetic and real-world data from Twitter and Flixster show that our method significantly outperforms the state-of-the-art methods. We argue that our algorithm can be implemented by parallel batch processing, in this way meeting the needs in terms of efficiency and scalability of real-world applications.