Skip to main content

Table 1 Core differences between model architectures

From: Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction

Model

Hidden states

Denotion of neighbourhood

Message aggregation scheme

MPNN

\(h_{v}^{\left( t \right)}\)

\(N\left( v \right)\)

\(m_{v}^{\left( t \right)} = \mathop \sum \limits_{w \in N\left( v \right)} M_{t} \left( {h_{v}^{\left( t \right)} ,h_{w}^{\left( t \right)} ,e_{vw} } \right)\)

AMPNN

\(h_{v}^{\left( t \right)}\)

\(N\left( v \right)\)

\(m_{v}^{\left( t \right)} = A_{t} \left( {h_{v}^{\left( t \right)} ,S_{v}^{\left( t \right)} } \right)\), where

\(S_{v}^{\left( t \right)} = \left\{ {\left( {h_{w}^{\left( t \right)} ,e_{vw} } \right) | w \in N\left( v \right)} \right\}\)

EMNN

\(h_{vw}^{\left( t \right)}\)

\(\left\{ {\left( {k,v} \right) | k \in N\left( v \right),k \ne w} \right\}\)

\(m_{vw}^{\left( t \right)} = A_{t} \left( {e_{vw} ,S_{vw}^{\left( t \right)} } \right)\), where

\(S_{vw}^{\left( t \right)} = \left\{ {h_{kv} | k \in N\left( v \right), k \ne w} \right\}\)