Deep Learning Models for Graphs
In recent years, the field of machine learning has witnessed a paradigm shift with the emergence of graph neural networks (GNNs) as powerful tools for addressing prediction tasks on graph-structured data. Here, we'll delve into the transformative potential of GNNs, highlighting their role as optimizable transformations capable of handling diverse graph attributes, such as nodes, edges, and global context while preserving crucial graph symmetries, particularly permutation invariances.
The foundation of GNNs lies in the message-passing neural network (MPNN) framework. Through this framework, GNNs leverage a sophisticated mechanism for information exchange and aggregation across graph structures, enabling the model to capture intricate relationships and dependencies within the data.
One distinctive feature of GNNs is their adherence to a graph-in, graph-out architecture. This means that the model accepts a graph as input, equipped with information...
