OWNC: Open-World Node Classification on Graphs with a Dual-Embedding Interaction Framework
Abstract
:1. Introduction
- Imbalanced learning: It is common for the number of nodes with different labels to be imbalanced in the open-world setting due to the inherent diversity of the data. However, current models are ineffective in dealing with such imbalanced data.
- Too many or too few nodes from unseen classes: When dealing with too many or too few nodes from the unseen classes, the classification is usually less effective.
- We introduce a dual-embedding interaction training framework that enhances classification by effectively managing hard-to-learn samples, promoting model diversity through mutual sample selection, and reducing overfitting. These features collectively improve the model’s robustness and generalization, particularly in complex open-world scenarios.
- By integrating a GAN-based generator–discriminator architecture, our model maintains sensitivity to unseen classes, delivering strong performance regardless of whether there is a small or large number of nodes from unseen classes. This setup also mitigates imbalanced learning, further supporting the model’s ability to generalize.
- Our algorithm achieves significant performance improvements over state-of-the-art methods across three benchmark datasets, demonstrating its effectiveness in handling open-world node classification challenges.
2. Related Work
2.1. Open-World Learning
2.2. Graph Neural Networks
2.3. Co-Training Related Methods
2.4. Generator and Discriminator
3. Problem Definition and Framework Structure
3.1. Problem Definition
3.2. Framework Structure
4. Framework Structure
4.1. Open-World Classifier Learning
4.2. Graph Autoencoder Model
4.3. Labeled Loss Function
4.4. Unlabeled Loss
4.5. Open-World Node Classification
4.6. Algorithm Description
Algorithm 1: OWNC algorithm |
Date: : a graph with edges and node features; , , where S are the seen classes that appear in , and U are the unseen classes; C: the number of seen classes. Step: 1: // Graph Encoder Model 2: // For the first layer: 3: 5: 9: 11: 14: Obtain the label loss using Equation (10) 15: Obtain the unlabeled loss using Equation (17) 16: Back-propagate loss gradient using Equation (1) 17: |
5. Experimental Setup
- OWNC¬D: A variant of OWNC with the dual-embedding interaction training framework module removed.
- OWNC¬G: A variant of OWNC with the GAN module removed.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Name | Nodes | Edges | Features | Classes |
---|---|---|---|---|
Cora | 2708 | 5429 | 1433 | 7 |
CiteSeer | 3327 | 4732 | 3703 | 6 |
Dblp | 17,716 | 105,734 | 1639 | 4 |
Unseen Class = 1 | Unseen Class = 2 | Unseen Class = 3 | ||||
---|---|---|---|---|---|---|
Methods | ACC | F1 | ACC | F1 | ACC | F1 |
GCN | 0.652 | 0.530 | 0.633 | 0.533 | 0.544 | 0.500 |
GCN soft | 0.663 | 0.535 | 0.644 | 0.537 | 0.545 | 0.491 |
GCN sigmoid | 0.729 | 0.554 | 0.658 | 0.549 | 0.509 | 0.456 |
GCN-sigmoid threshold | 0.763 | 0.678 | 0.696 | 0.608 | 0.622 | 0.541 |
GCN-softmax threshold | 0.771 | 0.776 | 0.737 | 0.673 | 0.631 | 0.622 |
GCN-DOC | 0.787 | 0.756 | 0.775 | 0.739 | 0.673 | 0.697 |
GCN openmax | 0.796 | 0.700 | 0.743 | 0.688 | 0.635 | 0.641 |
OpenWGL | 0.833 | 0.835 | 0.790 | 0.781 | 0.778 | 0.752 |
0.855 | 0.855 | 0.820 | 0.800 | 0.556 | 0.655 | |
OWNC | 0.916 | 0.911 | 0.901 | 0.867 | 0.891 | 0.824 |
Unseen Class = 1 | Unseen Class = 2 | Unseen Class = 3 | ||||
---|---|---|---|---|---|---|
Methods | ACC | F1 | ACC | F1 | ACC | F1 |
GCN | 0.665 | 0.505 | 0.531 | 0.453 | 0.374 | 0.382 |
GCN softmax | 0.666 | 0.505 | 0.540 | 0.482 | 0.373 | 0.380 |
GCN sigmoid | 0.572 | 0.407 | 0.491 | 0.375 | 0.344 | 0.382 |
GCN-softmax threshold | 0.688 | 0.576 | 0.559 | 0.513 | 0.405 | 0.408 |
GCN-sigmoid threshold | 0.732 | 0.658 | 0.570 | 0.533 | 0.600 | 0.599 |
GCN-DOC | 0.675 | 0.617 | 0.715 | 0.658 | 0.664 | 0.587 |
GCN openmax | 0.691 | 0.581 | 0.600 | 0.571 | 0.473 | 0.493 |
OpenWGL | 0.700 | 0.654 | 0.753 | 0.618 | 0.723 | 0.496 |
0.733 | 0.632 | 0.780 | 0.660 | 0.493 | 0.404 | |
OWNC | 0.822 | 0.793 | 0.868 | 0.778 | 0.873 | 0.716 |
Unseen Class = 1 | Unseen Class = 2 | |||
---|---|---|---|---|
Methods | ACC | F1 | ACC | F1 |
GCN | 0.584 | 0.532 | 0.441 | 0.391 |
GCN softmax | 0.577 | 0.521 | 0.438 | 0.389 |
GCN sigmoid | 0.592 | 0.539 | 0.436 | 0.388 |
GCN-softmax threshold | 0.602 | 0.604 | 0.434 | 0.548 |
GCN-sigmoid threshold | 0.610 | 0.556 | 0.441 | 0.392 |
GCN_DOC | 0.698 | 0.632 | 0.588 | 0.542 |
GCN openmax | 0.578 | 0.529 | 0.495 | 0.485 |
OpenWGL | 0.743 | 0.742 | 0.760 | 0.753 |
0.751 | 0.700 | 0.716 | 0.654 | |
OWNC | 0.811 | 0.816 | 0.794 | 0.788 |
Unseen Class = 1 | Unseen Class = 2 | Unseen Class = 3 | ||||
---|---|---|---|---|---|---|
Methods | ACC | F1 | ACC | F1 | ACC | F1 |
OWNC¬G | 0.913 | 0.902 | 0.889 | 0.842 | 0.884 | 0.813 |
OWNC¬D | 0.790 | 0.800 | 0.796 | 0.784 | 0.810 | 0.764 |
OWNC | 0.916 | 0.911 | 0.901 | 0.867 | 0.891 | 0.827 |
Unseen Class = 1 | Unseen Class = 2 | Unseen Class = 3 | ||||
---|---|---|---|---|---|---|
Methods | ACC | F1 | ACC | F1 | ACC | F1 |
OWNC¬G | 0.817 | 0.792 | 0.860 | 0.773 | 0.872 | 0.686 |
OWNC¬D | 0.671 | 0.608 | 0.751 | 0.589 | 0.759 | 0.527 |
OWNC | 0.822 | 0.793 | 0.868 | 0.778 | 0.873 | 0.716 |
Unseen Class = 1 | Unseen Class = 2 | |||
---|---|---|---|---|
Methods | ACC | F1 | ACC | F1 |
OWNC¬G | 0.801 | 0.808 | 0.776 | 0.762 |
OWNC¬D | 0.654 | 0.655 | 0.660 | 0.646 |
OWNC | 0.811 | 0.816 | 0.794 | 0.788 |
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Chen, Y.; Wang, C. OWNC: Open-World Node Classification on Graphs with a Dual-Embedding Interaction Framework. Mathematics 2025, 13, 1475. https://doi.org/10.3390/math13091475
Chen Y, Wang C. OWNC: Open-World Node Classification on Graphs with a Dual-Embedding Interaction Framework. Mathematics. 2025; 13(9):1475. https://doi.org/10.3390/math13091475
Chicago/Turabian StyleChen, Yuli, and Chun Wang. 2025. "OWNC: Open-World Node Classification on Graphs with a Dual-Embedding Interaction Framework" Mathematics 13, no. 9: 1475. https://doi.org/10.3390/math13091475
APA StyleChen, Y., & Wang, C. (2025). OWNC: Open-World Node Classification on Graphs with a Dual-Embedding Interaction Framework. Mathematics, 13(9), 1475. https://doi.org/10.3390/math13091475