AANE_Python
- Accelerated Attributed Network Embedding, SDM 2017
- A General Embedding Framework for Heterogeneous Information Learning in Large-Scale Networks, TKDD, 2018
Installation
- Requirements
- numpy
- scipy
- Usage
- cd AANE_Python
- pip install -r requirements.txt
- python Runme.py
Input and Output
- Input: dataset such as "BlogCatalog.mat" and "Flickr.mat"
- Output: Embedding.mat, with "H_AANE" denotes the attributed network embedding, and "H_Net" denotes the network embedding
Code in Python
from AANE_fun import AANE_fun
H = AANE_fun(Net,Attri,d)
H = AANE_fun(Net,Attri,d,lambd,rho)
H = AANE_fun(Net,Attri,d,lambd,rho,'Att')
H = AANE_fun(Net,Attri,d,lambd,rho,'Att',splitnum)
- H is the joint embedding representation of Net and Attri;
- Net is the weighted adjacency matrix;
- Attri is the node attribute information matrix with row denotes nodes;
- splitnum is the number of pieces we split the SA for limited cache.
- Python 3.6.3 or 2.7.13 is recommended.
Reference in BibTeX:
@conference{Huang-etal17Accelerated,
Author = {Xiao Huang and Jundong Li and Xia Hu},
Booktitle = {SIAM International Conference on Data Mining},
Pages = {633--641},
Title = {Accelerated Attributed Network Embedding},
Year = {2017}}
@article{Huang-etal18A,
Title = {A General Embedding Framework for Heterogeneous Information Learning in Large-Scale Networks},
Author = {Xiao Huang and Jundong Li and Na Zou and Xia Hu},
Booktitle = {ACM Transactions on Knowledge Discovery from Data},
Volume = {12},
Year = {2018}}
Code for Distributed Computing
from AANE_fun_distri import AANE_fun
H = AANE_fun(Net,Attri,d)
H = AANE_fun(Net,Attri,d,lambd,rho)
H = AANE_fun(Net,Attri,d,lambd,rho,'Att')
H = AANE_fun(Net,Attri,d,lambd,rho,'Att',splitnum, worknum)
- H is the joint embedding representation of Net and Attri;
- Net is the weighted adjacency matrix;
- Attri is the node attribute information matrix with row denotes nodes;
- splitnum is the number of pieces we split the SA for limited cache;
- worknum is the number of worker.
The function for distributed computing could only be run on macOS with Python 3.6.3 recommended.