Papers › Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing

Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing

3 Feb 2023arXiv:2302.01524archive 2025-07-28

Yunchong Song, Chenghu Zhou, Xinbing Wang, Zhouhan Lin

Most graph neural networks follow the message passing mechanism. However, it faces the over-smoothing problem when multiple times of message passing is applied to a graph, causing indistinguishable node representations and prevents the model to effectively learn dependencies between farther-away nodes. On the other hand, features of neighboring nodes with different labels are likely to be falsely mixed, resulting in the heterophily problem. In this work, we propose to order the messages passing into the node representation, with specific blocks of neurons targeted for message passing within specific hops. This is achieved by aligning the hierarchy of the rooted-tree of a central node with the ordered neurons in its node representation. Experimental results on an extensive set of datasets show that our model can simultaneously achieve the state-of-the-art in both homophily and heterophily settings, without any targeted design. Moreover, its performance maintains pretty well while the model becomes really deep, effectively preventing the over-smoothing problem. Finally, visualizing the gating vectors shows that our model learns to behave differently between homophily and heterophily settings, providing an explainable graph neural model.

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load_twitch_gamer lumia-group/orderedgnn/datasets/datasets_linkx/load_data.py official repository ran · our draft was wrong MIT (permissive) · d4e6fd9f6cd8a3ad · report
compat_matrix lumia-group/orderedgnn/datasets/datasets_linkx/homophily.py official repository unverified MIT (permissive) · 606e13d2f58cd737 · report
edge_homophily lumia-group/orderedgnn/datasets/datasets_linkx/homophily.py official repository unverified MIT (permissive) · 07270c9ae73d7519 · report
even_quantile_labels lumia-group/orderedgnn/datasets/datasets_linkx/data_utils.py official repository unverified MIT (permissive) · be162bddd27d9fac · report
get_metric lumia-group/orderedgnn/task_node.py official repository unverified MIT (permissive) · c63f69f76b82bf84 · report
load_fb100 lumia-group/orderedgnn/datasets/datasets_linkx/load_data.py official repository unverified MIT (permissive) · 0765a875204d931d · report
load_twitch lumia-group/orderedgnn/datasets/datasets_linkx/load_data.py official repository unverified MIT (permissive) · 563a5ce4dce55a17 · report
node_homophily lumia-group/orderedgnn/datasets/datasets_linkx/homophily.py official repository unverified MIT (permissive) · 42ea8a2b9adf3c0e · report
rand_train_test_idx lumia-group/orderedgnn/datasets/datasets_linkx/data_utils.py official repository unverified MIT (permissive) · 13234652741e2264 · report
to_planetoid lumia-group/orderedgnn/datasets/datasets_linkx/data_utils.py official repository unverified MIT (permissive) · 01e5c1887f86d840 · report

Tasks

Node Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Node Classification Actor Ordered GNN Accuracy 37.99 ± 1.00 #9 of 62 Archive leaderboard report
Node Classification Chameleon Ordered GNN Accuracy 72.28±2.29 #22 of 61 Archive leaderboard report
Node Classification Cornell Ordered GNN Accuracy 87.03±4.73 #5 of 60 Archive leaderboard report
Node Classification Squirrel Ordered GNN Accuracy 62.44±1.96 #21 of 59 Archive leaderboard report
Node Classification Texas Ordered GNN Accuracy 86.22±4.12 #22 of 62 Archive leaderboard report
Node Classification Wisconsin Ordered GNN Accuracy 88.04±3.63 #21 of 63 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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