Papers › GNNDLD: Graph Neural Network with Directional Label Distribution
GNNDLD: Graph Neural Network with Directional Label Distribution
Chandramani Chaudhary, Nirmal Kumar Boran, N Sangeeth, and Virendra Singh
By leveraging graph structure, Graph Neural Networks (GNN) have emerged as a useful model for graph-based datasets. While it is widely assumed that GNNs outperform basic neural networks, recent research shows that for some datasets, neural networks outperform GNNs. Heterophily is one of the primary causes of GNN performance degradation, and many models have been proposed to handle it. Furthermore, some intrinsic information in graph structure is often overlooked, such as edge direction. In this work, we propose GNNDLD, a model which exploits the edge direction and label distribution around a node in varying neighborhoods (hop-wise). We combine features from all layers to retain both low-pass frequency and high-pass frequency components of a node because different layers of neural networks provide different types of information. In addition, to avoid oversmoothing, we decouple the node feature aggregation and transformation operations. By combining all of these concepts, we present a simple yet very efficient model. Experiments on six standard real-world datasets show the superiority of GNNDLD over the state-of-the-art models in both homophily and heterophily.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | Chameleon (60%/20%/20% random splits) | GNNDLD | 1:1 Accuracy | 79.78±1.66 | #1 of 38 | Archive leaderboard | report |
| Node Classification | CiteSeer (60%/20%/20% random splits) | GNNDLD | 1:1 Accuracy | 86.3±1.24 | #1 of 33 | Archive leaderboard | report |
| Node Classification | Cora (60%/20%/20% random splits) | GNNDLD | 1:1 Accuracy | 92.99 ±0.9 | #1 of 33 | Archive leaderboard | report |
| Node Classification | Film (60%/20%/20% random splits) | GNNDLD | 1:1 Accuracy | 75.69±0.78 | #1 of 37 | Archive leaderboard | report |
| Node Classification | PubMed (60%/20%/20% random splits) | GNNDLD | 1:1 Accuracy | 91.95±0.19 | #1 of 37 | Archive leaderboard | report |
| Node Classification | Squirrel (60%/20%/20% random splits) | GNNDLD | 1:1 Accuracy | 77.72±0.84 | #1 of 37 | 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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