A Simple and Scalable Graph Neural Network for Large Directed Graphs
This work addresses node classification for graph analysis researchers, offering an incremental improvement with a holistic method that adapts to dataset characteristics.
The paper tackled the problem of node classification in directed graphs by benchmarking various GNN combinations of node representations and edge direction awareness, finding no single stable SOTA and proposing A2DUG, which improves accuracy by up to 11.29% compared to SOTA methods.
Node classification is one of the hottest tasks in graph analysis. Though existing studies have explored various node representations in directed and undirected graphs, they have overlooked the distinctions of their capabilities to capture the information of graphs. To tackle the limitation, we investigate various combinations of node representations (aggregated features vs. adjacency lists) and edge direction awareness within an input graph (directed vs. undirected). We address the first empirical study to benchmark the performance of various GNNs that use either combination of node representations and edge direction awareness. Our experiments demonstrate that no single combination stably achieves state-of-the-art results across datasets, which indicates that we need to select appropriate combinations depending on the dataset characteristics. In response, we propose a simple yet holistic classification method A2DUG which leverages all combinations of node representations in directed and undirected graphs. We demonstrate that A2DUG stably performs well on various datasets and improves the accuracy up to 11.29 compared with the state-of-the-art methods. To spur the development of new methods, we publicly release our complete codebase under the MIT license.