https://github.com/annamalai-nr/subgraph2vec_gensim

Contains the code (and working vm setup) for our KDD MLG 2016 paper titled: "subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs"
https://github.com/annamalai-nr/subgraph2vec_gensim

deep-learning gensim graph-kernels kernel-methods word2vec

Last synced: about 2 months ago
JSON representation

Contains the code (and working vm setup) for our KDD MLG 2016 paper titled: "subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs"

• Host: GitHub
• URL: https://github.com/annamalai-nr/subgraph2vec_gensim
• Owner: annamalai-nr
• Created: 2017-04-20T16:02:45.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-04-29T10:30:31.000Z (over 7 years ago)
• Last Synced: 2024-07-24T03:13:15.451Z (2 months ago)
• Topics: deep-learning, gensim, graph-kernels, kernel-methods, word2vec
• Language: Python
• Homepage:
• Size: 15.8 MB
• Stars: 42
• Watchers: 7
• Forks: 18
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-graph-classification - [Python High Performance

README

        
 # subgraph2vec

The subgraph2vec paper could be found at: https://arxiv.org/pdf/1606.08928.pdf 

## Virtual Machine with working subgraph2vec setup ##

* An Ubuntu 16.04 vitural machine with all the libraries required for running subgraph2vec could be downloaded from: https://drive.google.com/open?id=0B5UPYObHDu7iWEE0ZlV5WXlhOGs

* Credentials of the VM: username = sg2vec, password = sg2vec

* Just clone this repository and follow the instructions mentioned below to run subgraph2vec on this VM

* NOTE: This VM contains a older version of this repository in its home folder. So, please delete it and clone afresh for accessing the latest code. 

#### Dependencies

This code is developed in python 2.7. It is ran and tested on Ubuntu 14.04 and 16.04.

It uses the following python packages:

1. gensim (version == 0.12.4)

2. networkx (version <= 1.11)

3. joblib (version <= 0.10.3)

4. scikit-learn (+scipy, +numpy)

Subgraph2vec is developed on top of "gensim" python package.

We have edited the native word2vec_inner.py file and created a new .so file named 'mod_word2vec_inner.so'.

NOTE: tensorflow version of the same will be released soon.

#####  The procedure for setting up subgraph2vec is as follows:

	1. git clone the repository (command: git clone https://github.com/MLDroid/subgraph2vec.git )

	2. sudo run init.sh (installs gensim, modifies word2vec libraries to use radial skipgram (refer sec: 5.2.2 of the paper))

	NOTE: no need to run init.sh on the sg2vec VM (it is already setup)

#####  The procedure for verifying the setup is as follows:

	1. make sure the gensim package contains a softlink "word2vec_inner.so -> mod_word2vec_inner.so" (command: ls -l /usr/local/lib/python2.7/dist-packages/gensim/models)

	2. make sure that the file named 'Annaword2vec.py' is present in /usr/local/lib/python2.7/dist-packages/gensim/models (command: ls /usr/local/lib/python2.7/dist-packages/gensim/models/Annaword2vec.py)

	3. make sure that the version of gensim is 0.12.4 (command from the python prompt: import gensim;print gensim.__version__)

#####  The procedure for obtaining rooted subgraph vectors using subgraph2vec approach is as follows:

	1. move to the folder "src-code" (command: cd src_code) (also make sure that kdd 2015 paper's (Deep Graph Kernels) datasets are available in '../kdd_datasets/dir_graphs/')

	2. run dump_wl_kernel_sentences.py file to generate the weisfeiler-lehman kernel's rooted subgraphs from all the graphs in a given folder

	   * syntax: python dump_wl_kernel_sentences.py   

	   * example: python dump_wl_kernel_sentences.py ../kdd_datasets/dir_graphs/mutag 4 8 (this will generate files with extension .WL4 that contain target and context subgraphs in every graph)

	3. run sg2vec.py to generate embeddings for each of the subgraphs generated in step 2

	   * syntax: python sg2vec.py      <# of cpu cores>

	   * example: python sg2vec.py ../kdd_datasets/dir_graphs/mutag WL4 mutag 32 100 8

	   * this will generate the following two outputs:

	      * i)  a gensim word2vec model containing embeddings for all the subgraphs in the "../models" folder

	      * ii) a json dump of the python dictionary containg embeddings of subgraphs (format of the dictionary: key = subgraph, value = embedding)

## Contact ##

In case of queries, please email: [email protected]

#### Reference

	@article{narayanansubgraph2vec,

	  title={subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs},

	  author={Narayanan, Annamalai and Chandramohan, Mahinthan and Chen, Lihui and Liu, Yang and Saminathan, Santhoshkumar}

	}