https://github.com/zw-zhang/powerlaw_tp

This is a sample implementation of "Power-law Distribution Aware Trust Prediction", IJCAI 2018.
https://github.com/zw-zhang/powerlaw_tp

matrix-factorization powerlaw trust-prediction

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

• Host: GitHub
• URL: https://github.com/zw-zhang/powerlaw_tp
• Owner: ZW-ZHANG
• Created: 2018-08-02T07:11:24.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-08-02T07:14:02.000Z (over 6 years ago)
• Last Synced: 2023-09-18T09:39:37.958Z (about 1 year ago)
• Topics: matrix-factorization, powerlaw, trust-prediction
• Language: Matlab
• Size: 1.95 KB
• Stars: 1
• Watchers: 0
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Trust Prediction

This is a sample implementation of "[Power-law Distribution Aware Trust Prediction](https://zw-zhang.github.io/files/2018_IJCAI_Trust.pdf)"(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}

}

```