How do GNNs handle heterogenous graphs with multiple types of nodes and edges?
Instruction: Describe the approaches and challenges in applying GNNs to heterogeneous graphs.
Updated
Example Answer
A heterogeneous graph contains different node or edge types, so I would preserve those types in the representation and message-passing rules. For a scholarly graph, authors, papers, and venues are different entities, and writes, cites, and published-in relations carry different meanings.
An R-GCN uses relation-specific transformations with aggregation over each relation. Parameter-sharing schemes can control the cost when there are many relations. A heterogeneous graph transformer uses type-aware attention and transformations to model these distinctions. Meta-path methods are another option, but a predefined meta-path is not mandatory for every heterogeneous GNN.
I would inspect feature dimensions, edge directions, and sparse relation coverage, then compare with simpler typed or homogeneous baselines. Combining all edges without their types may discard meaning; giving every rare relation a large independent parameter block can overfit. For link prediction, I would remove evaluation target edges and consider whether reverse edges reveal the same target fact. The schema and split are as important as the architecture.
Concrete Example
author → writes → paper and paper → cites → paper should not automatically share one untyped message function. They can share selected parameters if that is an explicit modeling choice.