Extended Data Fig. 1 |. Detailed view of individual BIONIC network encoder.

A more detailed view of an individual network encoder, including residual connections. A network specific graph convolutional network is used to encode the input network for increasing neighborhood sizes. The first GCN in the sequence learns features for a given node based on the node’s immediate neighborhood (1st order features). The next GCN learns features based on the node’s second order neighborhood (2nd order features), and so on. The node feature matrices learned by each GCN pass are summed together to create the final learned, network-specific features. Summing the outputs of the various GCNs in this way creates residual connections, allowing features from multiple neighborhood sizes to generate the final learned features, rather than just the final neighborhood size. This figure shows three GCN layers, but BIONIC uses the same pattern of connections for any number of GCN layers. Note that the GCN layers for a given encoder share their weights, so in effect, there is a single GCN layer for each encoder.