Graph Network

GINet layer

Graph Interaction Networks layer

This layer is inspired by Sazan Mahbub et al. “EGAT: Edge Aggregated Graph Attention Networks and Transfer Learning Improve Protein-Protein Interaction Site Prediction”, BioRxiv 2020

  1. Create edges feature by concatenating node feature

\[e_{ij} = LeakyReLu (a_{ij} * [W * x_i || W * x_j])\]

  1. Apply softmax function, in order to learn to consider or ignore some neighboring nodes

\[\alpha_{ij} = softmax(e_{ij})\]

  1. Sum over the nodes (no averaging here)

\[z_i = \sum_j (\alpha_{ij} * Wx_j + b_i)\]

Herein, we add the edge feature to the step 1)

\[e_{ij} = LeakyReLu (a_{ij} * [W * x_i || W * x_j || We * edge_{attr} ])\]

Fout Net layer

This layer is described by eq. (1) of “Protein Interface Predition using Graph Convolutional Network”, by Alex Fout et al. NIPS 2018

\[z = x_i * Wc + 1 / Ni Sum_j x_j * Wn + b\]

sGraphAttention (sGAT) layer

This is a new layer that is similar to the graph attention network but simpler

\[z_i = 1 / Ni Sum_j a_ij * [x_i || x_j] * W + b_i\]

|| is the concatenation operator: [1,2,3] || [4,5,6] = [1,2,3,4,5,6] Ni is the number of neighbor of node i Sum_j runs over the neighbors of node i \(a_ij\) is the edge attribute between node i and j

WGAT Conv layer