Generating Labeled Graphs Using Conditional Wasserstein GANs
Why this work is in the frame
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Bibliographic record
Abstract
Graph-structured data arises in many domains, from biological and chemical networks to social and knowledge graphs, where capturing both structural and class-specific patterns is critical. Generating realistic graphs conditioned on target class labels remains a challenging problem due to the discrete and irregular nature of graph topology. In this work, we propose a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for labeled graph generation. Our framework integrates class information at both the generator and discriminator, enabling controllable synthesis of graphs with desired properties. The generator maps random noise vectors and class embeddings to node feature and adjacency representations, while the discriminator leverages a Graph Neural Network to jointly evaluate graph authenticity and class consistency. We evaluate the approach on benchmark graph datasets, demonstrating its ability to generate structurally coherent and classconsistent graphs. Experimental results show improved stability, highlighting the framework's potential for applications in synthetic dataset augmentation, controlled graph generation, and downstream tasks. This project's source code is publicly available at https://github.com/ava-12/Labeled_Wasserstein_GANs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it