Initial commit

.gitignore

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+
+*.pyc

LICENCE

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+The MIT License
+
+Copyright (c) 2017 Thomas Kipf
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

README.md

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+Graph Convolutional Networks in PyTorch
+====
+
+PyTorch implementation of Graph Convolutional Networks (GCNs) for semi-supervised classification [1]. This makes use of the Cora dataset from [2].
+
+For a high-level introduction to GCNs, see:
+
+Thomas Kipf, [Graph Convolutional Networks](http://tkipf.github.io/graph-convolutional-networks/) (2016)
+
+![Graph Convolutional Networks](figure.png)
+
+## Installation
+
+```python setup.py install```
+
+## Requirements
+
+  * PyTorch 0.2.0
+  * Python 2.7
+
+## Usage
+
+```python train.py```
+
+## References
+
+[1] [Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016](https://arxiv.org/abs/1609.02907)
+
+[2] [Sen et al., Collective Classification in Network Data, AI Magazine 2008](http://linqs.cs.umd.edu/projects/projects/lbc/)
+
+## Cite
+
+Please cite our paper if you use this code in your own work:
+
+```
+@article{kipf2016semi,
+  title={Semi-Supervised Classification with Graph Convolutional Networks},
+  author={Kipf, Thomas N and Welling, Max},
+  journal={arXiv preprint arXiv:1609.02907},
+  year={2016}
+}
+```

data/cora/README

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+This directory contains the a selection of the Cora dataset (www.research.whizbang.com/data).
+
+The Cora dataset consists of Machine Learning papers. These papers are classified into one of the following seven classes:
+		Case_Based
+		Genetic_Algorithms
+		Neural_Networks
+		Probabilistic_Methods
+		Reinforcement_Learning
+		Rule_Learning
+		Theory
+
+The papers were selected in a way such that in the final corpus every paper cites or is cited by atleast one other paper. There are 2708 papers in the whole corpus. 
+
+After stemming and removing stopwords we were left with a vocabulary of size 1433 unique words. All words with document frequency less than 10 were removed.
+
+
+THE DIRECTORY CONTAINS TWO FILES:
+
+The .content file contains descriptions of the papers in the following format:
+
+		<paper_id> <word_attributes>+ <class_label>
+
+The first entry in each line contains the unique string ID of the paper followed by binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last entry in the line contains the class label of the paper.
+
+The .cites file contains the citation graph of the corpus. Each line describes a link in the following format:
+
+		<ID of cited paper> <ID of citing paper>
+
+Each line contains two paper IDs. The first entry is the ID of the paper being cited and the second ID stands for the paper which contains the citation. The direction of the link is from right to left. If a line is represented by "paper1 paper2" then the link is "paper2->paper1".