CoRGi: Content-Rich Graph Neural Networks with Attention

• URL: http://arxiv.org/abs/2110.04866v1
• Date: Sun, 10 Oct 2021 17:54:30 GMT
• Title: CoRGi: Content-Rich Graph Neural Networks with Attention
• Authors: Jooyeon Kim, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Cheng Zheng, Miltiadis Allamanis
• Abstract summary: We present CoRGi, a graph neural network 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. We show that CoRGi is better at making edge-value predictions over existing methods, especially on sparse regions of the graph.
• Score: 12.339385456449659
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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