Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models

Author:

Shen Xu1ORCID,Wang Yili1ORCID,Zhou Kaixiong2ORCID,Pan Shirui3ORCID,Wang Xin1ORCID

Affiliation:

1. Jilin University, Changchun, China

2. Massachusetts Institute of Technology, Cambridge, MA, USA

3. Griffith University, Goldcoast, Australia

Funder

International Science and Technology Cooperation Program of Jilin Province

Science and Technology Development Program of Jilin Province

Foundation of the National Key Research and Development of China

National Natural Science Foundation of China

Publisher

ACM

Reference61 articles.

1. Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M Bronstein, and Haggai Maron. 2021. Equivariant subgraph aggregation networks. arXiv preprint arXiv:2110.02910 (2021).

2. Line graph neural networks for link prediction;Cai Lei;IEEE Transactions on Pattern Analysis and Machine Intelligence,2021

3. Xiaohui Chen Jiaxing He Xu Han and Liping Liu. 2023. Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. (2023).

4. Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. 2023. Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023).

5. How powerful are k-hop message passing graph neural networks;Feng Jiarui;Advances in Neural Information Processing Systems,2022

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