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Title: When Do GNNs Work: Understanding and Improving Neighborhood Aggregation
Graph Neural Networks (GNNs) have been shown to be powerful in a wide range of graph-related tasks. While there exists various GNN models, a critical common ingredient is neighborhood aggregation, where the embedding of each node is updated by referring to the embedding of its neighbors. This paper aims to provide a better understanding of this mechanisms by asking the following question: Is neighborhood aggregation always necessary and beneficial? In short, the answer is no. We carve out two conditions under which neighborhood aggregation is not helpful: (1) when a node's neighbors are highly dissimilar and (2) when a node's embedding is already similar with that of its neighbors. We propose novel metrics that quantitatively measure these two circumstances and integrate them into an Adaptive-layer module. Our experiments show that allowing for node-specific aggregation degrees have significant advantage over current GNNs.  more » « less
Award ID(s):
1741317 1704532 1618481
PAR ID:
10208512
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
IJCAI'20: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI} 2020
Volume:
2020
Issue:
1
Page Range / eLocation ID:
1303 to 1309
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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