Papers › PANDA: Expanded Width-Aware Message Passing Beyond Rewiring

PANDA: Expanded Width-Aware Message Passing Beyond Rewiring

6 Jun 2024arXiv:2406.03671archive 2025-07-28

Jeongwhan Choi, Sumin Park, Hyowon Wi, Sung-Bae Cho, Noseong Park

Recent research in the field of graph neural network (GNN) has identified a critical issue known as "over-squashing," resulting from the bottleneck phenomenon in graph structures, which impedes the propagation of long-range information. Prior works have proposed a variety of graph rewiring concepts that aim at optimizing the spatial or spectral properties of graphs to promote the signal propagation. However, such approaches inevitably deteriorate the original graph topology, which may lead to a distortion of information flow. To address this, we introduce an expanded width-aware (PANDA) message passing, a new message passing paradigm where nodes with high centrality, a potential source of over-squashing, are selectively expanded in width to encapsulate the growing influx of signals from distant nodes. Experimental results show that our method outperforms existing rewiring methods, suggesting that selectively expanding the hidden state of nodes can be a compelling alternative to graph rewiring for addressing the over-squashing.

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Code

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add_edge jeongwhanchoi/panda/preprocessing/fosr.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 8253415b0d9f44f0 · report
choose_edge_to_add jeongwhanchoi/panda/preprocessing/fosr.py official repository ran · honoured contract MIT (permissive) · 063fa9897760a77f · report
compute_degrees jeongwhanchoi/panda/preprocessing/fosr.py official repository ran · fixture could not drive it MIT (permissive) · 64f9409122eaca34 · report
dirichlet_energy jeongwhanchoi/panda/measure_smoothing.py official repository ran MIT (permissive) · 2aa246a3970f8113 · report
get_adj_matrix jeongwhanchoi/panda/preprocessing/digl.py official repository ran MIT (permissive) · 522df9dc671b43ec · report
get_ppr_matrix jeongwhanchoi/panda/preprocessing/digl.py official repository ran fingerprinted MIT (permissive) · 60f2d5a22cd623c3 · report
load_centrality jeongwhanchoi/panda/preprocessing/panda.py official repository ran fingerprinted MIT (permissive) · 55fec0a00b6ca04c · report
dirichlet_normalized jeongwhanchoi/panda/measure_smoothing.py official repository unverified MIT (permissive) · 57e036a3b1857000 · report
get_top_k_matrix jeongwhanchoi/panda/preprocessing/digl.py official repository unverified MIT (permissive) · 685534c936bc9f51 · report

Tasks

Graph ClassificationGraph Neural NetworkGraph RegressionNode Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Graph Classification COLLAB R-GIN + PANDA Accuracy 77.8% #21 of 39 Archive leaderboard report
Graph Classification COLLAB GIN + PANDA Accuracy 75.11% #25 of 39 Archive leaderboard report
Graph Classification COLLAB R-GCN + PANDA Accuracy 71.4% #31 of 39 Archive leaderboard report
Graph Classification COLLAB GCN + PANDA Accuracy 68.4% #34 of 39 Archive leaderboard report
Graph Classification ENZYMES R-GIN + PANDA Accuracy 53.1 #39 of 54 Archive leaderboard report
Graph Classification ENZYMES GIN + PANDA Accuracy 46.2 #44 of 54 Archive leaderboard report
Graph Classification ENZYMES R-GCN + PANDA Accuracy 43.9 #46 of 54 Archive leaderboard report
Graph Classification ENZYMES GCN + PANDA Accuracy 31.55 #50 of 54 Archive leaderboard report
Graph Classification IMDB-BINARY GIN + PANDA Accuracy 72.56 #2 of 8 Archive leaderboard report
Graph Classification IMDB-BINARY R-GIN + PANDA Accuracy 72.09 #3 of 8 Archive leaderboard report
Graph Classification IMDB-BINARY R-GCN + PANDA Accuracy 66.79 #7 of 8 Archive leaderboard report
Graph Classification IMDB-BINARY GCN + PANDA Accuracy 63.76 #8 of 8 Archive leaderboard report
Graph Classification MUTAG R-GCN + PANDA Accuracy 90.05% #23 of 74 Archive leaderboard report
Graph Classification MUTAG GIN + PANDA Accuracy 88.75% #34 of 74 Archive leaderboard report
Graph Classification MUTAG R-GIN + PANDA Accuracy 88.2% #42 of 74 Archive leaderboard report
Graph Classification MUTAG GCN + PANDA Accuracy 85.75% #60 of 74 Archive leaderboard report
Graph Classification PROTEINS R-GIN + PANDA Accuracy 76.17 #56 of 103 Archive leaderboard report
Graph Classification PROTEINS GCN + PANDA Accuracy 76 #57 of 103 Archive leaderboard report
Graph Classification PROTEINS R-GCN + PANDA Accuracy 76 #58 of 103 Archive leaderboard report
Graph Classification PROTEINS GIN + PANDA Accuracy 75.759 #60 of 103 Archive leaderboard report
Graph Classification Peptides-func GCN + PANDA AP 0.6028±0.0031 #40 of 44 Archive leaderboard report
Graph Classification REDDIT-BINARY R-GIN + PANDA Accuracy 91.36 #1 of 9 Archive leaderboard report
Graph Classification REDDIT-BINARY GIN + PANDA Accuracy 91.055 #3 of 9 Archive leaderboard report
Graph Classification REDDIT-BINARY GCN + PANDA Accuracy 80.69 #4 of 9 Archive leaderboard report
Graph Classification REDDIT-BINARY R-GCN + PANDA Accuracy 80.2 #5 of 9 Archive leaderboard report
Graph Regression Peptides-struct GCN + PANDA MAE 0.3272±0.0001 #34 of 39 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.

Methods

Graph Neural Network

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