{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:12:49Z","timestamp":1760148769706,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T00:00:00Z","timestamp":1685491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Province Public Welfare Technology Application Research Project","award":["LGF21F020013","2021C03142"],"award-info":[{"award-number":["LGF21F020013","2021C03142"]}]},{"name":"Zhejiang Province Key R&amp;D projects","award":["LGF21F020013","2021C03142"],"award-info":[{"award-number":["LGF21F020013","2021C03142"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Many real-world scenarios can be naturally modeled as heterogeneous graphs, which contain both symmetry and asymmetry information. How to learn useful knowledge from the graph has become one of the hot spots of research in artificial intelligence. Based on Metapath2vec algorithm, an improved Metapath2vec algorithm is presented, which combines Metapath random walk, used to capture semantics and structure information between different nodes of a heterogeneous network, and GloVe model to consider the advantage of global text representation. In order to verify the feasibility and effectiveness of the model, node clustering and link prediction experiments were conducted on the self-generated ideal dataset and the MOOC course data. The analysis of experimental data on these tasks shows that the Metapath\u2013GloVe algorithm learns consistently better embedding of heterogeneous nodes, and the algorithm improves the node embedding performance to better characterize the heterogeneous network structure and learn the characteristics of nodes, which proves the effectiveness and scalability of the proposed method in heterogeneous network mining tasks. It is also shown through extensive experiments that the Metapath\u2013GloVe algorithm is more efficient than the non-negative matrix decomposition algorithm (NMF), and it can obtain better clustering results and more accurate prediction results in the video recommendation task.<\/jats:p>","DOI":"10.3390\/sym15061178","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T01:40:56Z","timestamp":1685583656000},"page":"1178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A MOOC Course Data Analysis Based on an Improved Metapath2vec Algorithm"],"prefix":"10.3390","volume":"15","author":[{"given":"Congcong","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7224-9372","authenticated-orcid":false,"given":"Jing","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomin","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Science, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1822-6190","authenticated-orcid":false,"given":"Xiaobin","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingzhi","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"ref_1","first-page":"443","article-title":"Research progress of graph embedding algorithm","volume":"49","author":"Liu","year":"2022","journal-title":"J. Zhejiang Univ. Sci. Ed."},{"key":"ref_2","first-page":"808","article-title":"Graph embedding methods and applications: A review of research","volume":"48","author":"Qi","year":"2020","journal-title":"J. Electron."},{"key":"ref_3","unstructured":"Sun, Z., Deng, Z.H., Nie, J.Y., and Tang, J. (2019). Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1137\/20M1386062","article-title":"Understanding graph embedding methods and their applications","volume":"63","author":"Xu","year":"2021","journal-title":"SIAM Rev."},{"key":"ref_5","first-page":"2646","article-title":"A social network node classification method based on graph embedding and support vector machines","volume":"38","author":"Zhang","year":"2021","journal-title":"Comput. Appl. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108334","DOI":"10.1016\/j.patcog.2021.108334","article-title":"Graph clustering via variational graph embedding","volume":"122","author":"Guo","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhou, D., Zhu, H., Xu, T., Zha, R., Chen, E., and Xiong, H. (2021, January 14\u201318). Attentive heterogeneous graph embedding for job mobility prediction. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Virtual Event Singapore.","DOI":"10.1145\/3447548.3467388"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"51587","DOI":"10.1109\/ACCESS.2022.3174197","article-title":"Recommender systems based on graph embedding techniques: A review","volume":"10","author":"Deng","year":"2022","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1109\/TKDE.2018.2849727","article-title":"A survey on network embedding","volume":"31","author":"Cui","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_10","first-page":"415","article-title":"A survey on heterogeneous graph embedding: Methods, techniques, applications and sources","volume":"9","author":"Wang","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.knosys.2018.03.022","article-title":"Graph embedding techniques, applications, and performance: A survey","volume":"151","author":"Goyal","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"47753","DOI":"10.1109\/ACCESS.2020.3035162","article-title":"Score prediction algorithm combining deep learning and matrix factorization in sensor cloud systems","volume":"9","author":"Gong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ymeth.2020.05.002","article-title":"SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning","volume":"179","author":"Zeng","year":"2020","journal-title":"Methods"},{"key":"ref_14","first-page":"1","article-title":"A review of community discovery methods applying non-negative matrix decomposition models","volume":"10","author":"Li","year":"2016","journal-title":"Comput. Sci. Explor."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/TKDE.2016.2598561","article-title":"A survey of heterogeneous information network analysis","volume":"29","author":"Shi","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1017\/S1351324916000334","article-title":"Word2Vec","volume":"23","author":"Church","year":"2017","journal-title":"Nat. Lang. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"17022","DOI":"10.1021\/acsomega.9b04195","article-title":"MIPDH: A novel computational model for predicting microRNA-mRNA interactions by DeepWalk on a heterogeneous network","volume":"5","author":"Wong","year":"2020","journal-title":"ACS Omega"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"579","DOI":"10.3389\/fmicb.2020.00579","article-title":"Predicting microbe-disease association by learning graph representations and rule-based inference on the heterogeneous network","volume":"11","author":"Lei","year":"2020","journal-title":"Front. Microbiol."},{"key":"ref_19","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, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098036"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ma, L., and Zhang, Y. (November, January 29). Using Word2Vec to process big text data. Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA.","DOI":"10.1109\/BigData.2015.7364114"},{"key":"ref_21","first-page":"94","article-title":"Sentiment analysis of Chinese text based on rough sets and multi-channel word vectors","volume":"3","author":"Chen","year":"2020","journal-title":"Chin. J. Inf."},{"key":"ref_22","first-page":"155","article-title":"Advances in research on semantic representation of word vectors","volume":"5","author":"Li","year":"2019","journal-title":"Intell. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C.D. (2014, January 25\u201329). GloVe: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_24","first-page":"45","article-title":"Text classification based on GloVe and SVM","volume":"17","author":"Zheng","year":"2018","journal-title":"Softw. Guide"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kulkarni, S., Katariya, J.K., and Potika, K. (2020, January 7\u201310). Glovenor: Glove for node representations with second order random walks. Proceedings of the 2020 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, The Hague, The Netherlands.","DOI":"10.1109\/ASONAM49781.2020.9381347"},{"key":"ref_26","first-page":"164","article-title":"Coauthorship prediction based on meta-paths and node attributes","volume":"57","author":"Zhang","year":"2021","journal-title":"Comput. Eng. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yu, J., Luo, G., Xiao, T., Zhong, Q., Wang, Y., Feng, W., and Tang, J. (2020, January 5\u201310). MOOCCube: A large-scale data repository for NLP applications in MOOCs. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.","DOI":"10.18653\/v1\/2020.acl-main.285"},{"key":"ref_28","first-page":"566","article-title":"Adagrad\u2014An optimizer for stochastic gradient descent","volume":"6","author":"Lydia","year":"2019","journal-title":"Int. J. Inf. Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.knosys.2015.03.027","article-title":"Spectral co-clustering ensemble","volume":"84","author":"Huang","year":"2015","journal-title":"Knowl. Based Syst."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/6\/1178\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:46:30Z","timestamp":1760125590000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/6\/1178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,31]]},"references-count":29,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["sym15061178"],"URL":"https:\/\/doi.org\/10.3390\/sym15061178","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2023,5,31]]}}}