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

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.

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

• Python >= 3.6
• Tensorflow >= 1.0
• NetworkX 2.0
• Numpy >= 1.13, Matplotlib >= 2.1
• Optparse

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

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