KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #ai-agent #developer-tools #codex #typescript #cli #deepseek-harness #claude-code #ai-agents #mcp #rust

graph_cnn

★ 49 Open GitHub ↗

Supplementary code to "Convolutional Neural Networks Generalization Utilizing the Data Graph Structure"

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# A generalization of Convolutional Neural Networks to Graph-Structured Data

This is supplementary code to "A generalization of Convolutional Neural Networks to Graph-Structured Data", by Yotam Hechtlinger, Purvasha Chakravarti and Jining Qin.

------------------

### Basic example
The graph convolution is implemented as a Keras layer, receiving as an argument an index matrix denoting the nodes proximity according to the expected number of visits. The implementation details are discussed in the paper.
```python
from kears.models import Sequential
from graph_convolution import GraphConv

g_model = Sequential()
g_model.add(GraphConv(filters=filters, neighbors_ix_mat=neighbors_ix_mat, 
                      num_neighbors=num_neighbors, input_shape=(1000,1)))
```

------------------

### Merck Dataset
The DPP4 dataset is part of the Merck Molecular Activity Challenge, a previous [Kaggle](https://www.kaggle.com/c/MerckActivity) competition. The data used here was downloaded from the [supplementary material](http://pubs.acs.org/doi/suppl/10.1021/ci500747n) of the paper "Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships" by Ma et al.

------------------

### Dependencies
Requires Keras version 2.0.0 or higher running the Theano backend.

graph_cnn

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README