https://github.com/rctzeng/ICML19-EgoCNN

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)
https://github.com/rctzeng/ICML19-EgoCNN

convolutional-neural-networks graph-convolutional-neural-networks graph-embeddings graph-networks graph-neural-networks icml icml-2019 interpretability scale-free-networks self-similarity

Last synced: 19 days ago
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Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

• Host: GitHub
• URL: https://github.com/rctzeng/ICML19-EgoCNN
• Owner: rctzeng
• License: mit
• Created: 2019-03-11T03:22:10.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-08-24T08:06:03.000Z (about 3 years ago)
• Last Synced: 2024-10-13T23:01:41.036Z (about 1 month ago)
• Topics: convolutional-neural-networks, graph-convolutional-neural-networks, graph-embeddings, graph-networks, graph-neural-networks, icml, icml-2019, interpretability, scale-free-networks, self-similarity
• Language: Python
• Homepage: http://proceedings.mlr.press/v97/tzeng19a.html
• Size: 164 KB
• Stars: 19
• Watchers: 1
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

Awesome Lists containing this project

• awesome-graph-classification - [Python Reference

README

        
# Ego-CNN

This is the repo for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures", Ruo-Chun Tzeng, Shan-Hung Wu, In Proceedings of ICML 2019.

 * [slides](https://drive.google.com/open?id=1ypDgm_EVsJCjC0c5Rl7dBvzQSRjSbkXZ)

In the paper, we proposed **Ego-Convolution** layer, which keeps the nice properties of Convolution layer to the graph including:

 * detection of location-invariant patterns

 * enlarged receptive fields in multi-layer architecture

 * [most importantly] detection of **precise** patterns

This enables our Ego-CNN to provide explanation to its prediction when jointly learned with a task.

![picture](figs/model-EgoCNN.png)

 1. In effect, Ego-CNN with L layers can detect patterns up-to L-hop ego-networks.

 2. By using the existing CNN visualization techniques such as Transposed Convolution or Grad-CAM variants, we can visualize the detected patterns in a specific filter or a specific neuron.

 3. By tying the weight of filter across different layers, our Ego-CNN is regularized to detect **self-similar** patterns

## Dependence

 * Python >= 3.6

 * Tensorflow >= 1.0

 * NetworkX 2.0

 * Numpy >= 1.13, Matplotlib >= 2.1

 * Optparse

## To Reproduce Our Result On ICML'19

### Step 1. Download and Preprocess Graph Classification Datasets

Execute Command `python download_dataset.py` to download all the bioinformatic and social network datasets used in the paper.

## Step 2. Train Ego-CNN on specified datasets for specified tasks

To reproduce ...

 * Graph Classification Experiments: run `./execute-graph-classification-on-benchmarks.sh`

 * Effectiveness of Scale-Free Regularizer: run `./execute-graph-classification-on-benchmarks.sh`

 * Visualization on synthetic compounds: run `./execute-graph-classification-on-benchmarks.sh`