Papers › A Simple and Scalable Graph Neural Network for Large Directed Graphs

A Simple and Scalable Graph Neural Network for Large Directed Graphs

14 Jun 2023arXiv:2306.08274archive 2025-07-28

Seiji Maekawa, Yuya Sasaki, Makoto Onizuka

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.

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normalize_string seijimaekawa/a2dug/src/layers.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · ff7d55c8c0dd7dde · report
sparse_mx_to_torch_sparse_tensor seijimaekawa/a2dug/src/process_spmm_dire.py official repository ran · our draft was wrong MIT (permissive) · c97b99c4e8201a97 · report
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cheb_poly seijimaekawa/a2dug/src/hermitian.py official repository unverified MIT (permissive) · 8a8364667e96b5a8 · report
decomp seijimaekawa/a2dug/src/hermitian.py official repository unverified MIT (permissive) · e0d3510a22e03470 · report
encode_onehot seijimaekawa/a2dug/src/utils_general.py official repository unverified MIT (permissive) · 8f0f3f9102b8868f · report
even_quantile_labels seijimaekawa/a2dug/src/data_utils.py official repository unverified MIT (permissive) · be162bddd27d9fac · report
hermitian_decomp seijimaekawa/a2dug/src/hermitian.py official repository unverified MIT (permissive) · 723d27760d2af154 · report
process seijimaekawa/a2dug/src/model.py official repository unverified MIT (permissive) · 71e05153a45879d4 · report
rand_train_test_idx seijimaekawa/a2dug/src/data_utils.py official repository unverified MIT (permissive) · 13234652741e2264 · report
resolver seijimaekawa/a2dug/src/layers.py official repository unverified MIT (permissive) · a24b6d4b9d32db82 · report
to_planetoid seijimaekawa/a2dug/src/data_utils.py official repository unverified MIT (permissive) · 01e5c1887f86d840 · report

Tasks

ClassificationGraph Neural NetworkNode Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Node Classification wiki A2DUG ACCURACY 65.13±0.07 #1 of 2 Archive leaderboard report

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