Large-Scale Heterogeneous Feature Embedding

Large-Scale Heterogeneous Feature Embedding, AAAI 2019

Installation

• Requirements

1. numpy
2. scipy
3. gensim
4. sklearn

• Usage

1. cd AANE_Python
2. pip install -r requirements.txt
3. python3.6 Runme.py

Input and Output

• Input: dataset such as "ACM.mat"
• Output: Embedding.mat, with "H_FeatWalk" denotes the joint heterogeneous feature embedding, and "H_FeatWalk_X" denotes the single feature embedding

Code in Python

from FeatWalk import featurewalk
H = featurewalk(featur1=Feature1, alpha1=.4, featur2=Feature2, alpha2=0.4, Net=Network, beta=0, num_paths=50, path_length=25, dim=100, win_size=5).function()

• featur1 is the first feature matrix
• alpha1 is the weight for the first feature matrix, 0 <= alpha1 <= 1
• featur2 is the second feature matrix
• alpha2 is the weight for the second feature matrix, with 0 <= alpha2 <= 1 and 0 <= alpha1+alpha2 <= 1
• Net is the last feature matrix, which describes the relations among instances, its weight is 1-alpha1-alpha2
• beta is the small value threshold
• num_paths is the number of feature walks to start at each instance
• path_length is the length of the feature walk started at each instance
• dim is the dimension of embedding representations
• win_size is the window size of skipgram model

Reference in BibTeX:

@conference{Huang-etal19Large,
Author = {Xiao Huang and Qingquan Song and Fan Yang and Xia Hu},
Booktitle = {AAAI Conference on Artificial Intelligence},
Title = {Large-Scale Heterogeneous Feature Embedding},
Year = {2019}}