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Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

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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.

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

  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