SLF

Python implementation of the method proposed in "Link Prediction with Signed Latent Factors in Signed Social Networks", Pinghua Xu, Wenbin Hu, Jia Wu and Bo Du, SIGKDD 2019.

NOTE This implementation can be used to solve both link prediction and sign prediction.

Overview

This repository is organised as follows:

• input/ contains four example graphs WikiElec WikiRfa Slashdot Epinions;
• output/ is the directory that stores the learned node embeddings;
• src/ contains the implementation of the proposed SLF method.

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

The model use a graph in .txt format as input. Every row indicates an edge between two nodes separated by a space or \t. The file does not contain a header. The node id should be a non-negative number. Four example graphs (donwloaded from SNAP, but node ID is resorted) WikiElec, WikiRfa, Slashdot and Epinions are included in the input/ directory. The structure of the input file is the following:

Source node	Target node	Sign
0	1	-1
1	3	1
1	2	1
2	4	-1

NOTE All the used graphs are directed. However, if you want to handle an undirected graph, modify your input file to make that each edge (u, v, s) constitutes two rows of the file like the following:

Source node	Target node	Sign
u	v	s
v	u	s

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

--epochs                    INT      Number of training epochs            Default==20
--k1                        INT      Positive SLF dimension               Default==32
--k2                        INT      Negative SLF dimension               Default==32
--p0                        FLOAT    Effect of no feedback                Default==0.001
--n                         INT      Number of noise samples              Default==5
--learning-rate             FLOAT    Leaning rate                         Default==0.025

Evaluation options

--test-size                 FLOAT    Test ratio                           Default==0.2
--split-seed                INT      Random seed for splitting dataset    Default==16
--link-prediction           BOOL     Make link prediction or not          Default=False
--sign-prediction           BOOL     Make sign prediction or not          Default=True

NOTE As sign prediction is a more popular evaluation task, --link-prediction is set to False and --sign-prediction is set to True by default. You can refer to our paper to find the difference between the two tasks.

Examples

Train an SLF 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 epoch number and test ratio:

python src/main.py --epochs 30 --test-size 0.3

Train an SLF model on the WikiRfa dataset, perform link prediction task but not sign prediction task:

python src/main.py --edge-path ./input/WikiRfa.txt --outward-embedding-path ./output/WikiElec_outward --inward-embedding-path ./output/WikiElec_inward --link-prediction True --sign-prediction False

If you want to learn node embedding for other use and not to waste time performing link prediction or sign prediction, then run:

python src/main.py --link-prediction False --sign-prediction False

Output

Tasks on signed networks

For sign prediction task, we use AUC and Macro-F1 for evaluation.

For link prediction task, we use AUC@p, AUC@n and AUC@non for evaluation. Refer to our paper for detailed description. We adimit that it is not a good choice to use Micro-F1 for evaluation on a dataset with unbalanced labels, so we removed this metric.

We perform the evaluation after each epoch, and output the provisional result like the following:

Epoch 0 Optimizing: 100%|██████████████████████████████████████| 6637/6637 [00:19<00:00, 343.23it/s]
Evaluating...
Sign prediction, epoch 0: AUC 0.832, F1 0.697
Link prediction, epoch 0: AUC@p 0.901, AUC@n 0.750, AUC@non 0.878
Epoch 1 Optimizing: 100%|██████████████████████████████████████| 6637/6637 [00:19<00:00, 345.80it/s]
Evaluating...
Sign prediction, epoch 1: AUC 0.858, F1 0.730
Link prediction, epoch 1: AUC@p 0.882, AUC@n 0.739, AUC@non 0.855

When the training is ended up, the evaluation results are printed in tabular format. If --sign-prediction==True, the results of sign prediction are printed like the following:

Epoch	AUC	Macro-F1
0	0.832	0.697
1	0.858	0.730
2	0.838	0.739
...	...	...
19	0.905	0.802

And if --link-prediction==True, the results of link prediction are printed like the following:

Epoch	AUC@p	AUC@n	AUC@non
0	0.901	0.750	0.878
1	0.882	0.739	0.855
2	0.885	0.762	0.867
...	...	...	...
19	0.943	0.920	0.948

Node embeddings

The learned embeddings are saved in output/ in .npz format (supported by Numpy). Note that if the maximal node ID is 36, then the embedding matrix has 36+1 rows ordered by node ID (as the ID can start from 0). Although some nodes may not exist (e.g., node 11 is removed from the original dataset), it does not matter.

You can use them for any purpose in addition to the two performed tasks.

Baselines

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

• SIGNet "Signet: Scalable embeddings for signed networks" [source]
• MF "Low rank modeling of signed networks"
• LSNE "Solving link-oriented tasks in signed network via an embedding approach"
• SIDE "Side: representation learning in signed directed networks" [source]

Cite

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

@inproceedings{xu2019link,
  title={Link prediction with signed latent factors in signed social networks},
  author={Xu, Pinghua and Hu, Wenbin and Wu, Jia and Du, Bo},
  booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={1046--1054},
  year={2019}
}