thrinayani39e / influence_receptivity_network Goto Github PK

Codes for recovering the influence-receptivity structure in networks

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.

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