Model-free inference of diffusion networks using RKHS embeddings


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.

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  1. 1.

    E.g., we set 20 as a minimal support threshold in our experiments, since inference on candidate edges with very few adoption time intervals would have little statistical significance.

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    Superscript (uv) on \(y_{c}\) is omitted as it remains the same for all time intervals in \(D^{(i)}\).

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    To support this intuition, we also show in Fig. 1 the distribution of adoption time intervals conditioned by movie popularity in our Flixster experimental dataset, for connected (blue) or unconnected (red) user pairs. For similar empirical evidence we also refer the reader to Du et al. (2013).

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    We are grateful to the authors for the binary package of NPDC and one synthetic dataset.

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    The proportion of pairs with edge and without edge in these batches should be very similar to the one of the entire dataset.

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Hu, S., Cautis, B., Chen, Z. et al. Model-free inference of diffusion networks using RKHS embeddings. Data Min Knowl Disc 33, 499–525 (2019).

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  • Diffusion networks
  • Edge inference
  • Clustering
  • Kernel embeddings