Message passing in graphs
Unlike traditional neural networks, GNNs need to account for the inherent structure of the graph, allowing nodes to exchange information and update their representations based on their local neighborhoods. This core mechanism is achieved through message passing, a process of iteratively passing messages between nodes and aggregating information from their neighbors.
GNNs operate on graph-structured data and use a message-passing mechanism to update node representations based on information from neighboring nodes. Let’s delve into the mathematical explanation of message passing in GNNs.
Consider an undirected graph 
, where 
is the set of nodes and
is the set of edges. Each node 
in 
has an associated feature vector 
. The goal of a GNN is to learn a representation 
for each node that captures information from its neighborhood.
The basic message-passing operation in a GNN can be broken down into a series of steps:
- Aggregation of messages...
