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Jul 12, 2020
Jul 12, 2020

DifNet (Deep Diffusive Neural Network)

Deep Diffusive Neural Network Model Architecture

Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification

Source code of "Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification".
Paper arXiv URL: https://arxiv.org/abs/2001.07922

Two other papers are helpful for readers to follow the ideas in this paper and the code

(1) FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network https://arxiv.org/abs/1805.08751

(2) Graph Neural Lasso for Dynamic Network Regression https://arxiv.org/abs/1907.11114

Graph Neural Networks from IFM Lab

The latest graph neural network models proposed by IFM Lab can be found via the following link
IFM Lab GNNs: https://github.com/jwzhanggy/IFMLab_GNN

References

@article{Zhang2020GetRO,
  title={Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification},
  author={Jiawei Zhang},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.07922}
}

How to run the code?

To run a script, you can just use command line: python3 script_name.py

After downloading the code, you can run

python3 script.py

directly for node classification on Cora dataset.

How to turn on/off the blocks?

You can change the "if 0" to "if 1" to turn on a script block, and the reverse to turn off a script block.

Several toolkits may be needed to run the code

(1) pytorch (https://anaconda.org/pytorch/pytorch) (2) sklearn (https://anaconda.org/anaconda/scikit-learn) (3) transformers (https://anaconda.org/conda-forge/transformers)


Organization of the code?

A simpler template of the code is also available at http://www.ifmlab.org/files/template/IFM_Lab_Program_Template_Python3.zip

The whole program is divided into five main parts:

(1) data.py (for data loading and basic data organization operators, defines abstract method load() )

(2) method.py (for complex operations on the data, defines abstract method run() )

(3) result.py (for saving/loading results from files, defines abstract method load() and save() )

(4) evaluate.py (for result evaluation, defines abstract method evaluate() )

(5) setting.py (for experiment settings, defines abstract method load_run_save_evaluate() )

The base class of these five parts are defined in ./code/base_class/, they are all abstract class defining the templates and architecture of the code.

The inherited class are provided in ./code, which inherit from the base classes, implement the abstract methonds.

Detailed information on funtional classes?

a. data

(1) DatasetLoader.py (for dataset loading)

b. method

(1) cellGDU.py (defines the GDU neuron, we also include several simplified version of GDU in this class)

(2) MethodDifNet.py (the DifNet model for node classification)

c. result

(1) ResultSaving.py (for saving results to file)

d. evaluate

(1) EvaluateAcc.py (accuracy metric)

e. setting

(1) Settings.py (defines the interactions and data exchange among the above classes)


GDU Neuron

GDU Neuron Architecture

In ./code/cellGDU.py , we provide the implementation of several different versions of GDU.

(1) gdu_type == 'original': the GDU introduced in the paper. It introduces two neural gates f and e for state updating, and two other neural gates g and r for output selection.

(2) gdu_type == 'short_gate': a simplified version of GDU. It also uses the same number of gates for state updating and output selection. The main simplification is on the output, which selects output about h and z, whereas x is a fixed output.

(3) gdu_type == 'single_gate': a simplified version of GDU. It uses two neural gates f and e for state updating, but only uses one single gate for output selection. It is also introduced in the paper as a simplified version of GDU.

(4) gdu_type == 'recombine': a simplified version of GDU. It further simplify the "short_gate" GDU in the output to be selected.

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Source code of DifNet.

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