BTWalk

This is the implementation of TKDE20 paper ''BTWalk: Branching Tree Random Walk for Multi-order Structured Network Embedding'' (a copy is in this repo.). All of the parameters and the corresponding descriptions in btvec.cpp, btvec.py, vec4cross.py can be found in Table 2 in the paper.

Requirements

• gsl c++ library To install gsl lib on your own machine, you can refer to this link (in Chinese).

Single-network embedding (BTVec)

To run network embedding for a single network (weighted and directed), you need a network file named $name$-net.txt, whose format is:

nid1 nid2 weight1
nid2 nid1 weight2
...

where nid should be integer and start from 0.

To get embedding, you need to run:

python2 btvec.py --input $name$-net.txt --dim 128

and the embedding will be output at ./emb/$name$-128.txt.

For more hyper-parameters to control the training procedure, please refer to our paper and btvec.py and btvec.cpp.

Cross-network embedding (Vec4Cross)

To run network embedding for crossing networks, you need two networks ${net1}$-net.txt and ${net2}$-net.txt, a partial alignment file ${net1}$2${net2}$_${portion}$, and a ground-truth alignment file ${net1}$2${net2}$_gt.. The format of network files is the same as the above. The format of alignment files ${net1}$2${net2}$_${portion}$ and ${net1}$2${net2}$_gt is:

nid_in_net1 nid_in_net2
...

For example, to get the embedding, you may run:

python2 vec4cross.py --net1 ./networks/f2t/fb-net.txt --net2 ./networks/f2t/tt-net.txt --seed ./networks/f2t/f2t_30 --gt ./networks/f2t/f2t_gt --name-dis ./networks/f2t/edit_dist_matrix.pickle

The embedding will be output at ./emb/${net1}$-${net2}$-emb-128.

Notes: the gamma used in the code is actually 1-gamma in the paper.

Reference

If you use the code, please cite:

@ARTICLE{btwalk,
  author={Xiong, Hao and Yan, Junchi},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={BTWalk: Branching Tree Random Walk for Multi-Order Structured Network Embedding}, 
  year={2022},
  volume={34},
  number={8},
  pages={3611-3628},
  doi={10.1109/TKDE.2020.3029061}}

If you use the data of crossing networks, facebook-twitter and douban-weibo, please cite:

@inproceedings{Cao2016ASNets,
author = {Cao, Xuezhi and Yu, Yong},
title = {ASNets: A Benchmark Dataset of Aligned Social Networks for Cross-Platform User Modeling},
year = {2016},
isbn = {9781450340731},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2983323.2983864},
doi = {10.1145/2983323.2983864},
booktitle = {Proceedings of the 25th ACM International on Conference on Information and Knowledge Management},
pages = {1881–1884},
numpages = {4},
keywords = {benchmark dataset, network alignment, user modeling},
location = {Indianapolis, Indiana, USA},
series = {CIKM ’16}
}