Skip to content

Ange1o/GPSP

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

35 Commits
GPSP-code
GPSP-code
 
 
img
img
 
 
README.md
README.md
 
 

Repository files navigation

GPSP

This is the code and supplementary materials for the paper appeared in WWW2018, GPSP: Graph Partition and Space Projection based approach for Heterogeneous Network Embedding.

The data and embeddings are aviable at https://drive.google.com/open?id=1PFp1E0O4I2LbitPo4_SV_0VP5hs2Z5gp.

Specification

GPSP-code

  • SubFolder preprocessing : preprocessing steps for the data
  • SubFolder pte : implmentation of pte
  • SubFolder space projection is the space projection step
  • SubFolder node classification : codes for nodes classification
  • SubFolder node clustering : codes for nodes clustering
  • SubFolder visulization : codes for modifying data for the embedding projector

img

  • Algorithm and visulazation images

Development Environment

  • OS: Ubuntu 16.04 LTS
  • Language: Python 3.5.2
  • CPU: Intel® Core™ i7-5820K CPU @ 3.30GHz × 12
  • RAM: 32GB
  • Libraries:
    • numpy 1.13.1
    • pandas 0.21.1
    • NLTK 3.2.5
    • scikit-learn 0.18.1

Algorithm

  • The followings are the summary of GPSP algorithm

alt text

alt text

alt text

Experimental Details

Visulization using Tensorflow Projector (2D t-sne)

  • The learned embeddings are fed into Tensorflow Projector using t-sne.
  • Below is the visulization of GPSP-DeepWalk.
  • Eight groups are Computing Systems, Theoretical Computer Science, Computer Networks & Wireless Communication, Computer Graphics, Human Computer Interaction, Computational Linguistics, Computer Vision & Pattern Recognition, Databases & Information Systems.

alt text

  • GPSPL == GPSP-LINE
  • GPSPD == GPSP-DeepWalk

Multi-label classification results (Micro-F1)

      Method         10%       20%       30%       40%       50%       60%       70%       80%       90%    
LINE-1st 0.7003 0.7069 0.7081 0.7087 0.7087 0.7084 0.7079 0.7087 0.7079
LINE-2nd 0.6436 0.6446 0.6457 0.6462 0.6463 0.6458 0.6456 0.6450 0.6470
LINE-1st+2nd 0.7062 0.7064 0.7067 0.7075 0.7074 0.7077 0.7062 0.7072 0.7075
GPSPL-author 1st 0.6390 0.6420 0.6430 0.6436 0.6439 0.6432 0.6426 0.6448 0.6455
GPSPL-author 2nd 0.6162 0.6179 0.6184 0.6186 0.6181 0.6181 0.6183 0.6199 0.6212
GPSPL-author 1st+2nd 0.6487 0.6509 0.6515 0.6519 0.6522 0.6515 0.6519 0.6534 0.6540
GPSPL-paper 1st 0.7118 0.7148 0.7136 0.7156 0.7167 0.7127 0.7219 0.7206 0.7227
GPSPL-paper 2nd 0.6532 0.6546 0.6553 0.6554 0.6546 0.6540 0.6552 0.6521 0.6565
GPSPL-paper 1st+2nd 0.7235 0.7247 0.7247 0.7252 0.7256 0.7250 0.7262 0.7256 0.7267
PTE 0.7122 0.7125 0.7129 0.7135 0.7133 0.7138 0.7140 0.7135 0.7138
metapath2vec 0.6546 0.6547 0.6549 0.6550 0.6547 0.6551 0.6552 0.6537 0.6529
metapath2vec++ 0.6692 0.6687 0.6681 0.6679 0.6676 0.6678 0.6677 0.6658 0.6651
Deepwalk 0.6992 0.6998 0.7010 0.7008 0.6992 0.6988 0.6986 0.6964 0.6988
GPSPD-author 0.5919 0.5936 0.5950 0.5968 0.5963 0.5993 0.5974 0.5995 0.5980
GPSPD-paper 0.7010 0.7011 0.7016 0.7019 0.7021 0.7020 0.7018 0.7023 0.7020
GPSPD 0.7275 0.7304 0.7318 0.7330 0.7324 0.7328 0.7320 0.7331 0.7318
GPSPL 1st 0.7344 0.7378 0.7397 0.7396 0.7391 0.7401 0.7410 0.7425 0.7388
GPSPL 2nd 0.7121 0.7128 0.7141 0.7130 0.7148 0.7146 0.7137 0.7145 0.7159
GPSPL 1st+2nd 0.7512 0.7540 0.7557 0.7564 0.7564 0.7558 0.7554 0.7574 0.7552

Multi-label classification results (Macro-F1)

Method 10% 20% 30% 40% 50% 60% 70% 80% 90%
LINE-1st 0.6996 0.7050 0.7061 0.7069 0.7067 0.7062 0.7056 0.7063 0.7059
LINE-2nd 0.6389 0.6400 0.6413 0.6417 0.6419 0.6415 0.6409 0.6403 0.6426
LINE-1st+2nd 0.7032 0.7034 0.7036 0.7046 0.7043 0.7049 0.7035 0.7044 0.7036
GPSPL-author 1st 0.6399 0.6427 0.6434 0.6439 0.6438 0.6436 0.6424 0.6451 0.6451
GPSPL-author 2nd 0.6119 0.6136 0.6141 0.6143 0.6140 0.6138 0.6138 0.6162 0.6169
GPSPL-author 1st+2nd 0.6477 0.6498 0.6506 0.6507 0.6508 0.6501 0.6506 0.6529 0.6528
GPSPL-paper 1st 0.7087 0.7112 0.7099 0.7120 0.7130 0.7083 0.7198 0.7177 0.7211
GPSPL-paper 2nd 0.6557 0.6574 0.6580 0.6582 0.6571 0.6570 0.6578 0.6550 0.6591
GPSPL-paper 1st+2nd 0.7212 0.7226 0.7226 0.7230 0.7231 0.7229 0.7243 0.7232 0.7251
PTE 0.7089 0.7093 0.7094 0.7098 0.7101 0.7104 0.7090 0.7099 0.7094
metapath2vec 0.6307 0.6310 0.6313 0.6317 0.6322 0.6325 0.6328 0.6313 0.6301
metapath2vec++ 0.6478 0.6475 0.6473 0.6477 0.6478 0.6474 0.6473 0.6456 0.6445
Deepwalk 0.6964 0.6969 0.6982 0.6981 0.6965 0.6964 0.6963 0.6937 0.6961
GPSPD-author 0.5872 0.5887 0.5912 0.5922 0.5912 0.5977 0.5941 0.5971 0.5944
GPSPD-paper 0.7012 0.7015 0.7018 0.7020 0.7022 0.7021 0.7018 0.7023 0.7016
GPSPD 0.7253 0.7280 0.7290 0.7300 0.7298 0.7302 0.7295 0.7306 0.7289
GPSPL-1st 0.7318 0.7356 0.7369 0.7369 0.7361 0.7374 0.7388 0.7402 0.7364
GPSPL-2nd 0.7111 0.7117 0.7132 0.7119 0.7139 0.7137 0.7130 0.7136 0.7155
GPSPL-1st+2nd 0.7482 0.7513 0.7527 0.7534 0.7534 0.7529 0.7526 0.7544 0.7522

Node clustering results (NMI)

Method(Proximity) LINE PTE GPSPL-author GPSPL-paper GPSPL metapath2v metapath2v++ Deepwalk GPSPD-author GPSPD-paper GPSPD
1-st order(local) 0.3015 NA 0.2609 0.0447 0.1049 NA NA NA NA NA NA
2-nd order (global) 0.2529 0.2634 0.2505 0.2403 0.3118 0.2403 0.2473 0.2873 0.1681 0.3392 0.3555
1st+2nd order 0.2516 NA 0.2607 0.1738 0.1894 NA NA NA NA NA NA

About

Heterogeneous network embedding model

Resources

Readme
Activity

Stars

6 stars

Watchers

1 watching

Forks

1 fork
Report repository

Releases

No releases published

Packages

No packages published

Languages