ADMP-GNN: Adaptive Depth Message Passing GNN
This addresses a specific inefficiency in GNNs for graph learning tasks, but it is incremental as it builds on existing message-passing schemes.
The paper tackles the problem of GNNs using a fixed number of message-passing steps for all nodes, which is suboptimal because nodes have varying needs, and proposes ADMP-GNN to dynamically adjust layers per node, resulting in improved performance in node classification tasks.
Graph Neural Networks (GNNs) have proven to be highly effective in various graph learning tasks. A key characteristic of GNNs is their use of a fixed number of message-passing steps for all nodes in the graph, regardless of each node's diverse computational needs and characteristics. Through empirical real-world data analysis, we demonstrate that the optimal number of message-passing layers varies for nodes with different characteristics. This finding is further supported by experiments conducted on synthetic datasets. To address this, we propose Adaptive Depth Message Passing GNN (ADMP-GNN), a novel framework that dynamically adjusts the number of message passing layers for each node, resulting in improved performance. This approach applies to any model that follows the message passing scheme. We evaluate ADMP-GNN on the node classification task and observe performance improvements over baseline GNN models.