ZW-ZHANG / Powerlaw_TP

This is a sample implementation of "Power-law Distribution Aware Trust Prediction", IJCAI 2018.

Trust Prediction

This is a sample implementation of "Power-law Distribution Aware Trust Prediction"(IJCAI 2018).

Requirements

MATLAB (MATLAB 2017a works fine for me)

Usage

Run Powerlaw_TP with MATLAB

function [U,V,S] = Powerlaw_TP(A_input,k,beta,l1,l2,l3,l4,l5,iter,seed,use_GPU)
% Inputs:
    % A_input: n x n adjacency matrix
    % k: dimensionality
    % beta: coefficient for high-order proximity
    % l1,l2,l3,l4,l5: regularization parameters
    % iter: number of iterations
    % seed: random seed
    % use_GPU: whether to use GPU
% Outputs:
    % U: n x k matrix
    % V: k x k matrix
    % S: n x n matrix, sparse
% Objective function:	
% min_{U,V,S} ||(A - U * V * U' - S)||_F^2 + l1 * ||U||_F^2 + l2 * ||V||_F^2 + l3 * ||S||_F^2 + l4 * ||S||_1

Cite

If you find this code useful, please cite our paper:

@inproceedings{wang2018power,
  title={Power-law Distribution Aware Trust Prediction.},
  author={Wang, Xiao and Zhang, Ziwei and Wang, Jing and Cui, Peng and Yang, Shiqiang},
  booktitle={Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence},
  pages={3564--3570},
  year={2018}
}