{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T14:21:13Z","timestamp":1777299673425,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T00:00:00Z","timestamp":1679788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["22A0502"],"award-info":[{"award-number":["22A0502"]}]},{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["61772179"],"award-info":[{"award-number":["61772179"]}]},{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["2019JJ40005"],"award-info":[{"award-number":["2019JJ40005"]}]},{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["(2022) 351"],"award-info":[{"award-number":["(2022) 351"]}]},{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["2016TP1020"],"award-info":[{"award-number":["2016TP1020"]}]},{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["202250045231"],"award-info":[{"award-number":["202250045231"]}]},{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["2022HSKFJJ012"],"award-info":[{"award-number":["2022HSKFJJ012"]}]},{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["QL20210262"],"award-info":[{"award-number":["QL20210262"]}]},{"name":"National Natural Science Foundation of China","award":["22A0502"],"award-info":[{"award-number":["22A0502"]}]},{"name":"National Natural Science Foundation of China","award":["61772179"],"award-info":[{"award-number":["61772179"]}]},{"name":"National Natural Science Foundation of China","award":["2019JJ40005"],"award-info":[{"award-number":["2019JJ40005"]}]},{"name":"National Natural Science Foundation of China","award":["(2022) 351"],"award-info":[{"award-number":["(2022) 351"]}]},{"name":"National Natural Science Foundation of China","award":["2016TP1020"],"award-info":[{"award-number":["2016TP1020"]}]},{"name":"National Natural Science Foundation of China","award":["202250045231"],"award-info":[{"award-number":["202250045231"]}]},{"name":"National Natural Science Foundation of China","award":["2022HSKFJJ012"],"award-info":[{"award-number":["2022HSKFJJ012"]}]},{"name":"National Natural Science Foundation of China","award":["QL20210262"],"award-info":[{"award-number":["QL20210262"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["22A0502"],"award-info":[{"award-number":["22A0502"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["61772179"],"award-info":[{"award-number":["61772179"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["2019JJ40005"],"award-info":[{"award-number":["2019JJ40005"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["(2022) 351"],"award-info":[{"award-number":["(2022) 351"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["2016TP1020"],"award-info":[{"award-number":["2016TP1020"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["202250045231"],"award-info":[{"award-number":["202250045231"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["2022HSKFJJ012"],"award-info":[{"award-number":["2022HSKFJJ012"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["QL20210262"],"award-info":[{"award-number":["QL20210262"]}]},{"name":"14th Five-Year Plan Key Disciplines and Application-Oriented Special Disciplines of Hunan Province","award":["22A0502"],"award-info":[{"award-number":["22A0502"]}]},{"name":"14th Five-Year Plan Key Disciplines and Application-Oriented Special Disciplines of Hunan Province","award":["61772179"],"award-info":[{"award-number":["61772179"]}]},{"name":"14th Five-Year Plan Key Disciplines and Application-Oriented Special Disciplines of Hunan Province","award":["2019JJ40005"],"award-info":[{"award-number":["2019JJ40005"]}]},{"name":"14th Five-Year Plan Key Disciplines and Application-Oriented Special Disciplines of Hunan Province","award":["(2022) 351"],"award-info":[{"award-number":["(2022) 351"]}]},{"name":"14th Five-Year Plan Key Disciplines and Application-Oriented Special Disciplines of Hunan Province","award":["2016TP1020"],"award-info":[{"award-number":["2016TP1020"]}]},{"name":"14th Five-Year Plan Key Disciplines and Application-Oriented Special Disciplines of Hunan Province","award":["202250045231"],"award-info":[{"award-number":["202250045231"]}]},{"name":"14th Five-Year Plan Key Disciplines and Application-Oriented Special Disciplines of Hunan Province","award":["2022HSKFJJ012"],"award-info":[{"award-number":["2022HSKFJJ012"]}]},{"name":"14th Five-Year Plan Key Disciplines and Application-Oriented Special Disciplines of Hunan Province","award":["QL20210262"],"award-info":[{"award-number":["QL20210262"]}]},{"name":"Science and Technology Plan Project of Hunan Province","award":["22A0502"],"award-info":[{"award-number":["22A0502"]}]},{"name":"Science and Technology Plan Project of Hunan Province","award":["61772179"],"award-info":[{"award-number":["61772179"]}]},{"name":"Science and Technology Plan Project of Hunan Province","award":["2019JJ40005"],"award-info":[{"award-number":["2019JJ40005"]}]},{"name":"Science and Technology Plan Project of Hunan Province","award":["(2022) 351"],"award-info":[{"award-number":["(2022) 351"]}]},{"name":"Science and Technology Plan Project of Hunan Province","award":["2016TP1020"],"award-info":[{"award-number":["2016TP1020"]}]},{"name":"Science and Technology Plan Project of Hunan Province","award":["202250045231"],"award-info":[{"award-number":["202250045231"]}]},{"name":"Science and Technology Plan Project of Hunan Province","award":["2022HSKFJJ012"],"award-info":[{"award-number":["2022HSKFJJ012"]}]},{"name":"Science and Technology Plan Project of Hunan Province","award":["QL20210262"],"award-info":[{"award-number":["QL20210262"]}]},{"name":"Science and Technology Innovation Project of Hengyang","award":["22A0502"],"award-info":[{"award-number":["22A0502"]}]},{"name":"Science and Technology Innovation Project of Hengyang","award":["61772179"],"award-info":[{"award-number":["61772179"]}]},{"name":"Science and Technology Innovation Project of Hengyang","award":["2019JJ40005"],"award-info":[{"award-number":["2019JJ40005"]}]},{"name":"Science and Technology Innovation Project of Hengyang","award":["(2022) 351"],"award-info":[{"award-number":["(2022) 351"]}]},{"name":"Science and Technology Innovation Project of Hengyang","award":["2016TP1020"],"award-info":[{"award-number":["2016TP1020"]}]},{"name":"Science and Technology Innovation Project of Hengyang","award":["202250045231"],"award-info":[{"award-number":["202250045231"]}]},{"name":"Science and Technology Innovation Project of Hengyang","award":["2022HSKFJJ012"],"award-info":[{"award-number":["2022HSKFJJ012"]}]},{"name":"Science and Technology Innovation Project of Hengyang","award":["QL20210262"],"award-info":[{"award-number":["QL20210262"]}]},{"name":"Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University","award":["22A0502"],"award-info":[{"award-number":["22A0502"]}]},{"name":"Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University","award":["61772179"],"award-info":[{"award-number":["61772179"]}]},{"name":"Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University","award":["2019JJ40005"],"award-info":[{"award-number":["2019JJ40005"]}]},{"name":"Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University","award":["(2022) 351"],"award-info":[{"award-number":["(2022) 351"]}]},{"name":"Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University","award":["2016TP1020"],"award-info":[{"award-number":["2016TP1020"]}]},{"name":"Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University","award":["202250045231"],"award-info":[{"award-number":["202250045231"]}]},{"name":"Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University","award":["2022HSKFJJ012"],"award-info":[{"award-number":["2022HSKFJJ012"]}]},{"name":"Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University","award":["QL20210262"],"award-info":[{"award-number":["QL20210262"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["22A0502"],"award-info":[{"award-number":["22A0502"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["61772179"],"award-info":[{"award-number":["61772179"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["2019JJ40005"],"award-info":[{"award-number":["2019JJ40005"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["(2022) 351"],"award-info":[{"award-number":["(2022) 351"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["2016TP1020"],"award-info":[{"award-number":["2016TP1020"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["202250045231"],"award-info":[{"award-number":["202250045231"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["2022HSKFJJ012"],"award-info":[{"award-number":["2022HSKFJJ012"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["QL20210262"],"award-info":[{"award-number":["QL20210262"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while ignoring the preservation of the attribute information of nodes. Thus, the node attributes cannot be fully learned and the ability of the GAE to learn higher-quality representations is weakened. To address the issue, this paper proposes a novel GAE model that preserves node attribute similarity. The structural graph and the attribute neighbor graph, which is constructed based on the attribute similarity between nodes, are integrated as the encoder input using an effective fusion strategy. In the encoder, the attributes of the nodes can be aggregated both in their structural neighborhood and by their attribute similarity in their attribute neighborhood. This allows performing the fusion of the structural and node attribute information in the node representation by sharing the same encoder. In the decoder module, the adjacency matrix and the attribute similarity matrix of the nodes are reconstructed using dual decoders. The cross-entropy loss of the reconstructed adjacency matrix and the mean-squared error loss of the reconstructed node attribute similarity matrix are used to update the model parameters and ensure that the node representation preserves the original structural and node attribute similarity information. Extensive experiments on three citation networks show that the proposed method outperforms state-of-the-art algorithms in link prediction and node clustering tasks.<\/jats:p>","DOI":"10.3390\/e25040567","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T03:31:48Z","timestamp":1679887908000},"page":"567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Graph Autoencoder with Preserving Node Attribute Similarity"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4938-4407","authenticated-orcid":false,"given":"Mugang","family":"Lin","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China"},{"name":"Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang 421002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunhui","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanying","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huihuang","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China"},{"name":"Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang 421002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6114-0766","authenticated-orcid":false,"given":"Xianfang","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,26]]},"reference":[{"key":"ref_1","first-page":"52","article-title":"Representation learning on graphs: Methods and applications","volume":"40","author":"Hamilton","year":"2017","journal-title":"IEEE Database Eng. Bull."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","article-title":"A comprehensive survey of graph embedding: Problems, techniques, and applications","volume":"30","author":"Cai","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cao, S., Lu, W., and Xu, Q. (2015, January 19\u201323). Grarep: Learning graph representations with global structural information. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM 2015), Melbourne, VIC, Australia.","DOI":"10.1145\/2806416.2806512"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ou, M., Cui, P., Pei, J., Zhang, Z., and Zhu, W. (2016, January 13\u201317). Asymmetric transitivity preserving graph embedding. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939751"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., and Yang, S. (2017, January 4\u20139). Community preserving network embedding. Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI 2017), San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10488"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., and Skiena, S. (2014, January 24\u201327). Deepwalk: Online learning of social representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2014), New York, NY, USA.","DOI":"10.1145\/2623330.2623732"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. (2016, January 13\u201317). node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939754"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dong, Y., Chawla, N.V., and Swami, A. (2017, January 13\u201317). metapath2vec: Scalable representation learning for heterogeneous networks. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017), Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098036"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, D., Cui, P., and Zhu, W. (2016, January 13\u201317). Structural deep network embedding. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939753"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., and Mei, Q. (2015, January 18\u201322). Line: Large-scale information network embedding. Proceedings of the 24th International Conference on World Wide Web (WWW 2015), Florence, Italy.","DOI":"10.1145\/2736277.2741093"},{"key":"ref_11","unstructured":"Kipf, T.N., and Welling, M. (2017, January 24\u201326). Semi-supervised classification with graph convolutional networks. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), Toulon, France."},{"key":"ref_12","unstructured":"Kipf, T.N., and Welling, M. (2016). Variational graph auto-encoders. arXiv, preprint."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.inffus.2021.07.013","article-title":"Interpretable learning based dynamic graph convolutional networks for alzheimer\u2019s disease analysis","volume":"77","author":"Zhu","year":"2022","journal-title":"Inform. Fusion"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Park, J., Yoo, S., Park, J., and Kim, H.J. (March, January 22). Deformable graph convolutional networks. Proceedings of the 36th AAAI Conference on Artificial Intelligence, Virtual Event.","DOI":"10.1609\/aaai.v36i7.20765"},{"key":"ref_15","unstructured":"Dwivedi, V.P., Luu, A.T., Laurent, T., Bengio, Y., and Bresson, X. (2022, January 25\u201329). Graph Neural Networks with Learnable Structural and Positional Representations. Proceedings of the 10th International Conference on Learning Representations (ICLR 2022), Virtual Event."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, C., Pan, S., Long, G., Zhu, X., and Jiang, J. (2017, January 6\u201310). Mgae: Marginalized graph autoencoder for graph clustering. Proceedings of the 26th ACM on Conference on Information and Knowledge Management (CIKM 2017), Singapore.","DOI":"10.1145\/3132847.3132967"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., and Zhang, C. (2018, January 13\u201319). Adversarially regularized graph autoencoder for graph embedding. Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/362"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Salha, G., Limnios, S., Hennequin, R., Tran, V.A., and Vazirgiannis, M. (2019, January 3\u20137). Gravity-inspired graph autoencoders for directed link prediction. Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), Beijing, China.","DOI":"10.1145\/3357384.3358023"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, Z., Wang, F., Yao, K., Liang, J., and Wang, Z. (2022, January 21\u201325). Multi-Scale Variational Graph AutoEncoder for Link Prediction. Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM 2022), Tempe, AZ, USA.","DOI":"10.1145\/3488560.3498531"},{"key":"ref_20","first-page":"4110","article-title":"Graph regularized autoencoder and its application in unsupervised anomaly detection","volume":"44","author":"Ahmed","year":"2021","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.ins.2022.06.039","article-title":"Graph autoencoder-based unsupervised outlier detection","volume":"608","author":"Du","year":"2022","journal-title":"Inform. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jin, W., Derr, T., Wang, Y., MA, Y., Liu, Z., and Tang, J. (2021, January 8\u201312). Node similarity preserving graph convolutional networks. Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021), Jerusalem, Israel.","DOI":"10.1145\/3437963.3441735"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gao, H., and Huang, H. (2018, January 13\u201319). Deep attributed network embedding. Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/467"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106618","DOI":"10.1016\/j.knosys.2020.106618","article-title":"Deep attributed network representation learning of complex coupling and interaction","volume":"212","author":"Li","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.1109\/TKDE.2018.2819980","article-title":"Attributed social network embedding","volume":"30","author":"Liao","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jin, D., Li, B., Jiao, P., He, D., and Zhang, W. (2019, January 10\u201316). Network-specific variational auto-encoder for embedding in attribute networks. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, China.","DOI":"10.24963\/ijcai.2019\/370"},{"key":"ref_27","unstructured":"Yang, C., Liu, Z., Zhao, D., Sun, M., and Chang, E.Y. (2015, January 25\u201331). Network representation learning with rich text information. Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina."},{"key":"ref_28","unstructured":"Hamilton, W.L., Ying, R., and Leskovec, J. (2017, January 4\u20139). Inductive representation learning on large graphs. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_29","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (May, January 30). Graph Attention Networks. Proceedings of the 6th International Conference on Learning Representations (ICLR 2018), Vancouver, BC, Canada."},{"key":"ref_30","unstructured":"You, J., Ying, R., and Leskovec, J. (2019, January 9\u201315). Position-aware graph neural networks. Proceedings of the 36th International conference on machine learning (ICML 2019), Long Beach, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, T., Wang, Y., Yue, Z., Yang, Y., Tong, Y., and Bai, J. (March, January 22). Graph pointer neural networks. Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022), Virtual Event.","DOI":"10.1609\/aaai.v36i8.20864"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Park, J., Lee, M., Chang, H.J., Lee, K., and Choi, J.Y. (November, January 29). Symmetric graph convolutional autoencoder for unsupervised graph representation learning. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV 2019), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00662"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, X., Wang, H., Li, Z., and Zhang, S. (2020, January 4\u20136). Graph Autoencoder Combined with Attribute Information in Graph. Proceedings of the 6th International Conference on Big Data and Information Analytics (BigDIA 2020), Shenzhen, China.","DOI":"10.1109\/BigDIA51454.2020.00014"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107564","DOI":"10.1016\/j.knosys.2021.107564","article-title":"Dual-decoder graph autoencoder for unsupervised graph representation learning","volume":"234","author":"Sun","year":"2021","journal-title":"Knowledge-Based Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"108215","DOI":"10.1016\/j.patcog.2021.108215","article-title":"Graph convolutional autoencoders with co-learning of graph structure and node attributes","volume":"121","author":"Wang","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, J., Lu, G., and Wu, Z. (2022, January 21\u201325). Multi-View Graph Autoencoder for Unsupervised Graph Representation Learning. Proceedings of the 26th International Conference on Pattern Recognition (ICPR 2022), Montr\u00e9al, QC, Canada.","DOI":"10.1109\/ICPR56361.2022.9956484"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"109193","DOI":"10.1016\/j.asoc.2022.109193","article-title":"A2AE: Towards adaptive multi-view graph representation learning via all-to-all graph autoencoder architecture","volume":"125","author":"Sun","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s10618-010-0210-x","article-title":"Leveraging social media networks for classification","volume":"23","author":"Tang","year":"2011","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_39","unstructured":"Grover, A., Zweig, A., and Ermon, S. (2019, January 9\u201315). Graphite: Iterative generative modeling of graphs. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Salha, G., Hennequin, R., and Vazirgiannis, M. (2020, January 14\u201318). Simple and effective graph autoencoders with one-hop linear models. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, Ghent, Belgium.","DOI":"10.1007\/978-3-030-67658-2_19"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Huang, T., Pei, Y., Menkovski, V., and Pechenizkiy, M. (2021, January 13\u201317). On Generalization of Graph Autoencoders with Adversarial Training. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, Bilbao, Spain.","DOI":"10.1007\/978-3-030-86520-7_23"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, C., Pan, S., Hu, R., Long, G., Jiang, J., and Zhang, C. (2019, January 10\u201316). Attributed graph clustering: A deep attentional embedding approach. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, China.","DOI":"10.24963\/ijcai.2019\/509"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physa.2016.01.038","article-title":"Link prediction with node clustering coefficient","volume":"452","author":"Wu","year":"2016","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Su, W., Yuan, Y., and Zhu, M. (2015, January 27\u201330). A relationship between the average precision and the area under the ROC curve. Proceedings of the 2015 International Conference on The Theory of Information Retrieval (ICTIR 2015), Northampton, MA, USA.","DOI":"10.1145\/2808194.2809481"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Vinh, N.X., Epps, J., and Bailey, J. (2009, January 14\u201318). Information theoretic measures for clusterings comparison: Is a correction for chance necessary?. Proceedings of the 26th Annual International Conference on Machine Learning (ICML 2009), Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553511"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/4\/567\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:03:22Z","timestamp":1760123002000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/4\/567"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,26]]},"references-count":45,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["e25040567"],"URL":"https:\/\/doi.org\/10.3390\/e25040567","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,26]]}}}