Attentive Walk-Aggregating Graph Neural Networks

Understand

Graph neural networks (GNNs) have been shown to possess strong representation power, which can be exploited for downstream prediction tasks on graph-structured data, such as molecules and social networks.

• They typically learn representations by aggregating information from the $K$-hop neighborhood of individual vertices or from the enumerated walks in the graph.
• Prior studies have demonstrated the effectiveness of incorporating weighting schemes into GNNs; however, this has been primarily limited to $K$-hop neighborhood GNNs so far.
• In this paper, we aim to design an algorithm incorporating weighting schemes into walk-aggregating GNNs and analyze their effect.