Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913215807488000 |
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| author | Liu, Shengchao Wang, Chengpeng Lu, Jiarui Nie, Weili Wang, Hanchen Li, Zhuoxinran Zhou, Bolei Tang, Jian |
| author_facet | Liu, Shengchao Wang, Chengpeng Lu, Jiarui Nie, Weili Wang, Hanchen Li, Zhuoxinran Zhou, Bolei Tang, Jian |
| contents | Deep generative models (DGMs) have been widely developed for graph data. However, much less investigation has been carried out on understanding the latent space of such pretrained graph DGMs. These understandings possess the potential to provide constructive guidelines for crucial tasks, such as graph controllable generation. Thus in this work, we are interested in studying this problem and propose GraphCG, a method for the unsupervised discovery of steerable factors in the latent space of pretrained graph DGMs. We first examine the representation space of three pretrained graph DGMs with six disentanglement metrics, and we observe that the pretrained representation space is entangled. Motivated by this observation, GraphCG learns the steerable factors via maximizing the mutual information between semantic-rich directions, where the controlled graph moving along the same direction will share the same steerable factors. We quantitatively verify that GraphCG outperforms four competitive baselines on two graph DGMs pretrained on two molecule datasets. Additionally, we qualitatively illustrate seven steerable factors learned by GraphCG on five pretrained DGMs over five graph datasets, including two for molecules and three for point clouds. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_17123 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled Liu, Shengchao Wang, Chengpeng Lu, Jiarui Nie, Weili Wang, Hanchen Li, Zhuoxinran Zhou, Bolei Tang, Jian Machine Learning Artificial Intelligence Quantitative Methods Deep generative models (DGMs) have been widely developed for graph data. However, much less investigation has been carried out on understanding the latent space of such pretrained graph DGMs. These understandings possess the potential to provide constructive guidelines for crucial tasks, such as graph controllable generation. Thus in this work, we are interested in studying this problem and propose GraphCG, a method for the unsupervised discovery of steerable factors in the latent space of pretrained graph DGMs. We first examine the representation space of three pretrained graph DGMs with six disentanglement metrics, and we observe that the pretrained representation space is entangled. Motivated by this observation, GraphCG learns the steerable factors via maximizing the mutual information between semantic-rich directions, where the controlled graph moving along the same direction will share the same steerable factors. We quantitatively verify that GraphCG outperforms four competitive baselines on two graph DGMs pretrained on two molecule datasets. Additionally, we qualitatively illustrate seven steerable factors learned by GraphCG on five pretrained DGMs over five graph datasets, including two for molecules and three for point clouds. |
| title | Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled |
| topic | Machine Learning Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2401.17123 |