Data Mining and Knowledge Discovery

, Volume 33, Issue 2, pp 499–525 | Cite as

Model-free inference of diffusion networks using RKHS embeddings

  • Shoubo Hu
  • Bogdan CautisEmail author
  • Zhitang Chen
  • Laiwan Chan
  • Yanhui Geng
  • Xiuqiang He
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2019


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.


Diffusion networks Edge inference Clustering Kernel embeddings 



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Copyright information

© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The Chinese University of Hong KongShatinHong Kong
  2. 2.University of Paris-SudOrsayFrance
  3. 3.Huawei Noah’s Ark LabShatinHong Kong

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