  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.
