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Node classification is one of the hottest tasks in graph analysis. Thoughexisting studies have explored various node representations in directed andundirected graphs, they have overlooked the distinctions of their capabilitiesto capture the information of graphs. To tackle the limitation, we investigatevarious combinations of node representations (aggregated features vs. adjacencylists) and edge direction awareness within an input graph (directed vs.undirected). We address the first empirical study to benchmark the performanceof various GNNs that use either combination of node representations and edgedirection awareness. Our experiments demonstrate that no single combinationstably achieves state-of-the-art results across datasets, which indicates thatwe need to select appropriate combinations depending on the datasetcharacteristics. In response, we propose a simple yet holistic classificationmethod A2DUG which leverages all combinations of node representations indirected and undirected graphs. We demonstrate that A2DUG stably performs wellon various datasets and improves the accuracy up to 11.29 compared with thestate-of-the-art methods. To spur the development of new methods, we publiclyrelease our complete codebase under the MIT license.

A Simple and Scalable Graph Neural Network for Large Directed Graphs