  Graph representations of a target domain often project it to a set of
entities (nodes) and their relations (edges). However, such projections often
miss important and rich information. For example, in graph representations used
in missing value imputation, items - represented as nodes - may contain rich
textual information. However, when processing graphs with graph neural networks
(GNN), such information is either ignored or summarized into a single vector
representation used to initialize the GNN. Towards addressing this, we present
CoRGi, a GNN that considers the rich data within nodes in the context of their
neighbors. This is achieved by endowing CoRGi's message passing with a
personalized attention mechanism over the content of each node. This way, CoRGi
assigns user-item-specific attention scores with respect to the words that
appear in an item's content. We evaluate CoRGi on two edge-value prediction
tasks and show that CoRGi is better at making edge-value predictions over
existing methods, especially on sparse regions of the graph.
