{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:26:38Z","timestamp":1775607998515,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,2,2]],"date-time":"2018-02-02T00:00:00Z","timestamp":1517529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Basic Research Program of China","award":["2015CB358700"],"award-info":[{"award-number":["2015CB358700"]}]},{"name":"NSFC","award":["61561130160"],"award-info":[{"award-number":["61561130160"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,2,2]]},"DOI":"10.1145\/3159652.3159706","type":"proceedings-article","created":{"date-parts":[[2018,2,6]],"date-time":"2018-02-06T18:12:23Z","timestamp":1517940743000},"page":"459-467","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":615,"title":["Network Embedding as Matrix Factorization"],"prefix":"10.1145","author":[{"given":"Jiezhong","family":"Qiu","sequence":"first","affiliation":[{"name":"Tsinghua University & Microsoft Research, Beijing, China"}]},{"given":"Yuxiao","family":"Dong","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}]},{"given":"Hao","family":"Ma","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Kuansan","family":"Wang","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}]},{"given":"Jie","family":"Tang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2018,2,2]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00106"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_3_2_1_3_1","volume-title":"Tensor spectral clustering for partitioning higher-order network structures SDM","author":"Benson Austin R."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1137\/16M1074023"},{"key":"e_1_3_2_1_5_1","unstructured":"Matthew Brand and Kun Huang. 2003. A unifying theorem for spectral embedding and clustering. AISTATS. Matthew Brand and Kun Huang. 2003. A unifying theorem for spectral embedding and clustering. AISTATS."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_3_2_1_7_1","unstructured":"Shaosheng Cao Wei Lu and Qiongkai Xu. 2016. Deep Neural Networks for Learning Graph Representations. AAAI. 1145--1152. Shaosheng Cao Wei Lu and Qiongkai Xu. 2016. Deep Neural Networks for Learning Graph Representations. AAAI. 1145--1152."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783296"},{"key":"e_1_3_2_1_9_1","unstructured":"Dehua Cheng Yu Cheng Yan Liu Richard Peng and Shang-Hua Teng. 2015. Efficient sampling for Gaussian graphical models via spectral sparsification COLT. 364--390. Dehua Cheng Yu Cheng Yan Liu Richard Peng and Shang-Hua Teng. 2015. Efficient sampling for Gaussian graphical models via spectral sparsification COLT. 364--390."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098106"},{"key":"e_1_3_2_1_11_1","unstructured":"Fan R.K. Chung. 1997. Spectral graph theory. Number 92. American Mathematical Soc. Fan R.K. Chung. 1997. Spectral graph theory. Number 92. American Mathematical Soc."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","volume-title":"Networks, Crowds, and Markets: Reasoning about a Highly Connected World","author":"Easley David","DOI":"10.1017\/CBO9780511761942"},{"key":"e_1_3_2_1_14_1","volume-title":"JMLR","volume":"9","author":"Fan Rong-En","year":"2008"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1137\/140985160"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_17_1","unstructured":"William L. Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. NIPS. William L. Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. NIPS."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00098"},{"key":"e_1_3_2_1_19_1","volume-title":"Johnson","author":"Horn Roger A.","year":"1991"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556225"},{"key":"e_1_3_2_1_21_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2016"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Richard B. Lehoucq Danny C. Sorensen and Chao Yang. 1998. ARPACK users' guide: solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods. SIAM. Richard B. Lehoucq Danny C. Sorensen and Chao Yang. 1998. ARPACK users' guide: solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods. SIAM.","DOI":"10.1137\/1.9780898719628"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772755"},{"key":"e_1_3_2_1_24_1","unstructured":"Omer Levy and Yoav Goldberg. 2014. Neural Word Embedding as Implicit Matrix Factorization. NIPS. 2177--2185. Omer Levy and Yoav Goldberg. 2014. Neural Word Embedding as Implicit Matrix Factorization. NIPS. 2177--2185."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Hang Li Haozheng Wang Zhenglu Yang and Masato Odagaki. 2017. Variation Autoencoder Based Network Representation Learning for Classification ACL. 56. Hang Li Haozheng Wang Zhenglu Yang and Masato Odagaki. 2017. Variation Autoencoder Based Network Representation Learning for Classification ACL. 56.","DOI":"10.18653\/v1\/P17-3010"},{"key":"e_1_3_2_1_26_1","volume-title":"Paul erdos is eighty","author":"Lov\u00e1sz L\u00e1szl\u00f3","year":"1993"},{"key":"e_1_3_2_1_27_1","unstructured":"Qing Lu and Lise Getoor. 2003. Link-based Classification. In ICML. Qing Lu and Lise Getoor. 2003. Link-based Classification. In ICML."},{"key":"e_1_3_2_1_28_1","volume-title":"2013 a. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781","author":"Mikolov Tomas","year":"2013"},{"key":"e_1_3_2_1_29_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S. Corrado and Jeff Dean. 2013 b. Distributed Representations of Words and Phrases and their Compositionality. NIPS. 3111--3119. Tomas Mikolov Ilya Sutskever Kai Chen Greg S. Corrado and Jeff Dean. 2013 b. Distributed Representations of Words and Phrases and their Compositionality. NIPS. 3111--3119."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939751"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_32_1","volume-title":"Link mining: Models, algorithms, and applications","author":"Philip S. Yu"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.868688"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"A.N. Shiryaev and A. Lyasoff. 2012. Problems in Probability. Springer New York. showLCCN2012937045 A.N. Shiryaev and A. Lyasoff. 2012. Problems in Probability. Springer New York. showLCCN2012937045","DOI":"10.1007\/978-1-4614-3688-1"},{"key":"e_1_3_2_1_35_1","volume-title":"Xiaodong Wang, Xiaoqi Shi, et almbox..","author":"Stark Chris","year":"2010"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783307"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557109"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526738"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.3115\/1073445.1073478"},{"key":"e_1_3_2_1_41_1","volume-title":"Trefethen and David Bau III","author":"Lloyd","year":"1997"},{"key":"e_1_3_2_1_42_1","volume-title":"Mining multi-label data. Data mining and knowledge discovery handbook","author":"Tsoumakas Grigorios"},{"key":"e_1_3_2_1_43_1","volume-title":"CANE: Context-aware network embedding for relation modeling ACL.","author":"Tu Cunchao","year":"2017"},{"key":"e_1_3_2_1_44_1","unstructured":"Cunchao Tu Weicheng Zhang Zhiyuan Liu and Maosong Sun. 2016. Max-Margin DeepWalk: Discriminative Learning of Network Representation. IJCAI. 3889--3895. Cunchao Tu Weicheng Zhang Zhiyuan Liu and Maosong Sun. 2016. Max-Margin DeepWalk: Discriminative Learning of Network Representation. IJCAI. 3889--3895."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-007-9033-z"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_3_2_1_47_1","volume-title":"Chang","author":"Yang Cheng","year":"2015"},{"key":"e_1_3_2_1_48_1","unstructured":"Zhilin Yang William W. Cohen and Ruslan Salakhutdinov. 2016 a. Revisiting Semi-Supervised Learning with Graph Embeddings ICML. 40--48. Zhilin Yang William W. Cohen and Ruslan Salakhutdinov. 2016 a. Revisiting Semi-Supervised Learning with Graph Embeddings ICML. 40--48."},{"key":"e_1_3_2_1_49_1","unstructured":"Zhilin Yang Jie Tang and William W. Cohen. 2016 b. Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs. IJCAI. 2287--2293. Zhilin Yang Jie Tang and William W. Cohen. 2016 b. Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs. IJCAI. 2287--2293."}],"event":{"name":"WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining","location":"Marina Del Rey CA USA","acronym":"WSDM 2018","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3159652.3159706","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3159652.3159706","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:53:41Z","timestamp":1751331221000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3159652.3159706"}},"subtitle":["Unifying DeepWalk, LINE, PTE, and node2vec"],"short-title":[],"issued":{"date-parts":[[2018,2,2]]},"references-count":49,"alternative-id":["10.1145\/3159652.3159706","10.1145\/3159652"],"URL":"https:\/\/doi.org\/10.1145\/3159652.3159706","relation":{},"subject":[],"published":{"date-parts":[[2018,2,2]]},"assertion":[{"value":"2018-02-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}