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STNE

A Python implementation of the more flexible version of the method proposed in "Social trust Network Embedding", Pinghua Xu, Wenbin Hu, Jia Wu, Weiwei Liu, Bo Du and Jian Yang, ICDM 2019. In this implementation, one can use the co-occurance node pair as positive samples instead of pairwise edges.

Overview

This repository is organized as follows:

  • input/ contains four example graphs WikiEditor WikiElec WikiRfa Slashdot Epinions;
  • output/ is the directory to store the learned node embeddings;
  • src/ contains the implementation of the proposed STNE model.

Requirements

The implementation is tested under Python 3.7, with the folowing packages installed:

  • networkx==2.3
  • numpy==1.16.5
  • scikit-learn==0.21.3
  • texttable==1.6.2
  • tqdm==4.36.1

Input

We investigated social trust network, which can be represented by a directed signed (un)weighted graph, in this work.

The code takes an input graph in .txt format. Every row indicates an edge between two nodes separated by a space or \t. The file does not contain a header. Nodes can be indexed starting with any non-negative number. Five example graphs are included in the input/ directory. Among these graphs, WikiElec, WikiRfa, Slashdot and Epinions are donwloaded from SNAP, but node ID is resorted, and WikiEditor is generated according to the description in our paper. The structure of the input file is the following:

Source node Target node Weight
0 1 4
1 3 -5
1 2 2
2 4 -1

Options

Input and output options

--edge-path                 STR      Input file path                      Default=="./input/WikiElec.txt"
--outward-embedding-path    STR      Outward embedding path               Default=="./output/WikiElec_outward"
--inward-embedding-path     STR      Inward embedding path                Default=="./output/WikiElec_inward"

Model options

--dim                       INT      Dimension of latent factor vector    Default==32
--n                         INT      Number of noise samples              Default==5
--num_walks                 INT      Walks per node                       Default==20
--walk_len                  INT      Length per walk                      Default==10
--workers                   INT      Number of threads used for random walking    Default==4
--m                         FLOAT    Damping factor                       Default==1
--norm                      FLOAT    Normalization factor                 Default==0.01
--learning-rate             FLOAT    Leaning rate                         Default==0.02

Evaluation options

--test-size                 FLOAT    Test ratio                           Default==0.2
--split-seed                INT      Random seed for splitting dataset    Default==16

Examples

Train an STNE model on the deafult WikiElec dataset, output the performance on sign prediction task, and save the embeddings:

python src/main.py

Train an SLF model with custom split seed and test ratio:

python src/main.py --split_seed 20 --test-size 0.3

Train an SLF model on the WikiRfa dataset:

python src/main.py --edge-path ./input/WikiRfa.txt  --outward-embedding-path ./output/WikiRfa_outward --inward-embedding-path ./output/WikiRfa_inward

Output

We perform sign prediction to evaluate the node embeddings. And we use AUC and macro-F1 as evaluation metric. Although Micro-F1 is used in our paper, we admit that it is not a good choice for evaluation on a dataset with unbalanced labels.

Run python src/main.py, and the output is printed like the following:

Optimizing: 100%|█████████████████████████████████████████████| 75496/75496 [13:20<00:00, 94.32it/s]
Sign prediction: AUC 0.874, F1 0.747

Like the other methods in Skip-Gram family, we only perform one-epoch training.

Baselines

In our paper, we used the following methods for comparison:

  • N2V "node2vec: Scalable Feature Learning for Networks" [source]
  • LINE "LINE: Large-scale information network embedding" [source]
  • LSNE "Solving link-oriented tasks in signed network via an embedding approach"
  • MF "Low rank modeling of signed networks"
  • SIDE "Side: representation learning in signed directed networks" [source]
  • SIGNet "Signet: Scalable embeddings for signed networks" [source]

Cite

If you find this repository useful in your research, please cite our paper:

@INPROCEEDINGS{8970926,
  author={Pinghua Xu and Wenbin Hu and Jia Wu and Weiwei Liu and Bo Du and Jian Yang},
  booktitle={2019 IEEE International Conference on Data Mining (ICDM)},
  title={Social Trust Network Embedding},
  year={2019},
  pages={678-687}
}

Moreover, if you are interested in the topic of social trust network, you may want to know our another work "Opinion Maximization in Social Trust Networks" (IJCAI 2020).

Correction

Correction on a typo. Page 7, Sec. IV.A. "In the relationship prediction tasks, we randomly selected \alpha % of the relationships as the test set, and the remaining (100 - \alpha) % were used for training" -> "In the relationship prediction tasks, we randomly selected \alpha % of the relationships as the training set, and the remaining (100 - \alpha) % were used for test"