Graph Information Vanishing Phenomenon inImplicit Graph Neural Networks

Understand

One of the key problems of GNNs is how to describe the importance of neighbor nodes in the aggregation process for learning node representations.

• A class of GNNs solves this problem by learning implicit weights to represent the importance of neighbor nodes, which we call implicit GNNs such as Graph Attention Network.
• The basic idea of implicit GNNs is to introduce graph information with special properties followed by Learnable Transformation Structures (LTS) which encode the importance of neighbor nodes via a data-driven way.
• In this paper, we argue that LTS makes the special properties of graph information disappear during the learning process, resulting in graph information unhelpful for learning node representations.