Academic literature on the topic 'Variational graph auto-Encoder (VGAE)'
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Journal articles on the topic "Variational graph auto-Encoder (VGAE)":
Hui, Binyuan, Pengfei Zhu, and Qinghua Hu. "Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4215–22. http://dx.doi.org/10.1609/aaai.v34i04.5843.
Duan, Yuning, Jingdong Jia, Yuhui Jin, Haitian Zhang, and Jian Huang. "Expressway Vehicle Trajectory Prediction Based on Fusion Data of Trajectories and Maps from Vehicle Perspective." Applied Sciences 14, no. 10 (May 15, 2024): 4181. http://dx.doi.org/10.3390/app14104181.
Choong, Jun Jin, Xin Liu, and Tsuyoshi Murata. "Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization." Entropy 22, no. 2 (February 7, 2020): 197. http://dx.doi.org/10.3390/e22020197.
Ma, Weigang, Jing Wang, Chaohui Zhang, Qiao Jia, Lei Zhu, Wenjiang Ji, and Zhoukai Wang. "Application of Variational Graph Autoencoder in Traction Control of Energy-Saving Driving for High-Speed Train." Applied Sciences 14, no. 5 (February 29, 2024): 2037. http://dx.doi.org/10.3390/app14052037.
Zhang, Jing, Guangli Wu, and Shanshan Song. "Video Summarization Generation Based on Graph Structure Reconstruction." Electronics 12, no. 23 (November 23, 2023): 4757. http://dx.doi.org/10.3390/electronics12234757.
Zhang, Ying, Qi Zhang, Yu Zhang, and Zhiyuan Zhu. "VGAE-AMF: A Novel Topology Reconstruction Algorithm for Invulnerability of Ocean Wireless Sensor Networks Based on Graph Neural Network." Journal of Marine Science and Engineering 11, no. 4 (April 16, 2023): 843. http://dx.doi.org/10.3390/jmse11040843.
Patel, Neel, Nhat Le, Tan Nguyen, Fedaa Najdawi, Sandhya Srinivasan, Adam Stanford-Moore, Deeksha Kartik, et al. "Abstract 4912: Unsupervised detection of stromal phenotypes with distinct fibrogenic and inflamed properties in NSCLC." Cancer Research 84, no. 6_Supplement (March 22, 2024): 4912. http://dx.doi.org/10.1158/1538-7445.am2024-4912.
Shi, Han, Haozheng Fan, and James T. Kwok. "Effective Decoding in Graph Auto-Encoder Using Triadic Closure." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 906–13. http://dx.doi.org/10.1609/aaai.v34i01.5437.
Behrouzi, Tina, and Dimitrios Hatzinakos. "Graph variational auto-encoder for deriving EEG-based graph embedding." Pattern Recognition 121 (January 2022): 108202. http://dx.doi.org/10.1016/j.patcog.2021.108202.
Zhan, Junjian, Feng Li, Yang Wang, Daoyu Lin, and Guangluan Xu. "Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding." Applied Sciences 11, no. 5 (March 7, 2021): 2371. http://dx.doi.org/10.3390/app11052371.
Dissertations / Theses on the topic "Variational graph auto-Encoder (VGAE)":
Belhadj, Djedjiga. "Multi-GAT semi-supervisé pour l’extraction d’informations et son adaptation au chiffrement homomorphe." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0023.
This thesis is being carried out as part of the BPI DeepTech project, in collaboration with the company Fair&Smart, primarily looking after the protection of personal data in accordance with the General Data Protection Regulation (RGPD). In this context, we have proposed a deep neural model for extracting information in semi-structured administrative documents (SSDs). Due to the lack of public training datasets, we have proposed an artificial generator of SSDs that can generate several classes of documents with a wide variation in content and layout. Documents are generated using random variables to manage content and layout, while respecting constraints aimed at ensuring their similarity to real documents. Metrics were introduced to evaluate the content and layout diversity of the generated SSDs. The results of the evaluation have shown that the generated datasets for three SSD types (payslips, receipts and invoices) present a high diversity level, thus avoiding overfitting when training the information extraction systems. Based on the specific format of SSDs, consisting specifically of word pairs (keywords-information) located in spatially close neighborhoods, the document is modeled as a graph where nodes represent words and edges, neighborhood connections. The graph is fed into a multi-layer graph attention network (Multi-GAT). The latter applies the multi-head attention mechanism to learn the importance of each word's neighbors in order to better classify it. A first version of this model was used in supervised mode and obtained an F1 score of 96% on two generated invoice and payslip datasets, and 89% on a real receipt dataset (SROIE). We then enriched the multi-GAT with multimodal embedding of word-level information (textual, visual and positional), and combined it with a variational graph auto-encoder (VGAE). This model operates in semi-supervised mode, being able to learn on both labeled and unlabeled data simultaneously. To further optimize the graph node classification, we have proposed a semi-VGAE whose encoder shares its first layers with the multi-GAT classifier. This is also reinforced by the proposal of a VGAE loss function managed by the classification loss. Using a small unlabeled dataset, we were able to improve the F1 score obtained on a generated invoice dataset by over 3%. Intended to operate in a protected environment, we have adapted the architecture of the model to suit its homomorphic encryption. We studied a method of dimensionality reduction of the Multi-GAT model. We then proposed a polynomial approximation approach for the non-linear functions in the model. To reduce the dimensionality of the model, we proposed a multimodal feature fusion method that requires few additional parameters and reduces the dimensions of the model while improving its performance. For the encryption adaptation, we studied low-degree polynomial approximations of nonlinear functions, using knowledge distillation and fine-tuning techniques to better adapt the model to the new approximations. We were able to minimize the approximation loss by around 3% on two invoice datasets as well as one payslip dataset and by 5% on SROIE
Book chapters on the topic "Variational graph auto-Encoder (VGAE)":
Belhadj, Djedjiga, Abdel Belaïd, and Yolande Belaïd. "Improving Information Extraction from Semi-structured Documents Using Attention Based Semi-variational Graph Auto-Encoder." In Lecture Notes in Computer Science, 113–29. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-41679-8_7.
Amodeo, Carlo, Igor Fortel, Olusola Ajilore, Liang Zhan, Alex Leow, and Theja Tulabandhula. "Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder." In Lecture Notes in Computer Science, 406–15. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16431-6_39.
Conference papers on the topic "Variational graph auto-Encoder (VGAE)":
Xie, Qianqian, Jimin Huang, Pan Du, Min Peng, and Jian-Yun Nie. "Inductive Topic Variational Graph Auto-Encoder for Text Classification." In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.naacl-main.333.
Teng, Wenjun, Yong Li, and Sam Kwong. "Light Field Compression via a Variational Graph Auto-Encoder." In 2021 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2021. http://dx.doi.org/10.1109/icwapr54887.2021.9736152.
Wei, Jiwei, Yang Yang, Xing Xu, Yanli Ji, Xiaofeng Zhu, and Heng Tao Shen. "Graph-based variational auto-encoder for generalized zero-shot learning." In MMAsia '20: ACM Multimedia Asia. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3444685.3446283.
Jiang, Xinke, Zidi Qin, Jiarong Xu, and Xiang Ao. "Incomplete Graph Learning via Attribute-Structure Decoupled Variational Auto-Encoder." In WSDM '24: The 17th ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3616855.3635769.
Qiang, Jipeng, Yun Li, Yunhao Yuan, and Wei Liu. "Variational graph auto-encoder using triplets of nodes for preserving proximity." In 2018 International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3226116.3226129.
Mrabah, Nairouz, Mohamed Bouguessa, and Riadh Ksantini. "Escaping Feature Twist: A Variational Graph Auto-Encoder for Node Clustering." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/465.
Saffari, Mohsen, Mahdi Khodayar, Seyed Mohammad Jafar Jalali, Miadreza Shafie-khah, and Joao P. S. Catalao. "Deep Convolutional Graph Rough Variational Auto-Encoder for Short-Term Photovoltaic Power Forecasting." In 2021 International Conference on Smart Energy Systems and Technologies (SEST). IEEE, 2021. http://dx.doi.org/10.1109/sest50973.2021.9543326.
Qian, Yurong, Jingjing Zheng, Zhe Zhang, Ying Jiang, Jiaxuan Zhang, and Lei Deng. "CMIVGSD: circRNA-miRNA Interaction Prediction Based on Variational Graph Auto-Encoder and Singular Value Decomposition." In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669875.
wu, Xinxing, and Qiang Cheng. "Stabilizing and Enhancing Link Prediction through Deepened Graph Auto-Encoders." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/498.