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Description

Codes for recovering the influence-receptivity structure in networks

References

Ming Yu, Varun Gupta, and Mladen Kolar. “Estimation of a Low-rank Topic-Based Model for Information Cascades”, to appear in Journal of Machine Learning Research. Full paper is available on arXiv: https://arxiv.org/abs/1709.01919.

Please see also the following two closely related works:

  • Learning Influence-Receptivity Network Structure with Guarantee. AISTATS 2019.
  • An Influence-Receptivity Model for Topic based Information Cascades. ICDM 2017.

Run codes

The main codes are the following two:

  • Data_generation.m: generate true coefficient matrix B1_0, B2_0 and generate cascades
  • optimization.m: alternating proximal gradient descent

Run these two scripts to get the results. Other helper functions:

  • drchrnd.m: generate sample from Dirichlet distribution; this is used for topic weight generation
  • grad_B1.m: calculate gradient with respect to B1
  • grad_B2.m: calculate gradient with respect to B2
  • likeli.m: calculate the likelihood function value
  • likeli_community.m: calculate the likelihood function value
  • One_data.m: generate one data/cascade

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Estimation of a Low-rank Topic-Based Model for Information Cascades

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