{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:15:17Z","timestamp":1726042517784},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030299101"},{"type":"electronic","value":"9783030299118"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-29911-8_52","type":"book-chapter","created":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T01:03:32Z","timestamp":1566522212000},"page":"673-683","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Network Embedding by Resource-Allocation for Link Prediction"],"prefix":"10.1007","author":[{"given":"Xinghao","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunming","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xunjian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,23]]},"reference":[{"issue":"4","key":"52_CR1","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1109\/TNN.2007.912312","volume":"19","author":"Y Bengio","year":"2008","unstructured":"Bengio, Y., Sen\u00e9cal, J.S.: Adaptive importance sampling to accelerate training of a neural probabilistic language model. IEEE Trans. Neural Networks 19(4), 713\u2013722 (2008). https:\/\/doi.org\/10.1109\/TNN.2007.912312","journal-title":"IEEE Trans. Neural Networks"},{"issue":"1\u20137","key":"52_CR2","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/S0169-7552(98)00110-X","volume":"30","author":"S Brin","year":"1998","unstructured":"Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1\u20137), 107\u2013117 (1998). https:\/\/doi.org\/10.1016\/S0169-7552(98)00110-X","journal-title":"Comput. Netw. ISDN Syst."},{"issue":"1","key":"52_CR3","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.chemolab.2005.05.004","volume":"80","author":"CD Brown","year":"2006","unstructured":"Brown, C.D., Davis, H.T.: Receiver operating characteristics curves and related decision measures: a tutorial. Chemometr. Intell. Lab. Syst. 80(1), 24\u201338 (2006)","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"52_CR4","doi-asserted-by":"publisher","unstructured":"Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 891\u2013900. ACM (2015). https:\/\/doi.org\/10.1145\/2806416.2806512","DOI":"10.1145\/2806416.2806512"},{"key":"52_CR5","doi-asserted-by":"crossref","unstructured":"Chen, H., Perozzi, B., Hu, Y., Skiena, S.: HARP: hierarchical representation learning for networks, pp. 2127\u20132134 (2018)","DOI":"10.1609\/aaai.v32i1.11849"},{"key":"52_CR6","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1360\/N112017-00145","volume":"47","author":"TU Cunchao","year":"2017","unstructured":"Cunchao, T.U., et al.: Network representation learning: an overview. Sci. Sinica 47, 980\u2013996 (2017). https:\/\/doi.org\/10.1360\/N112017-00145","journal-title":"Sci. Sinica"},{"key":"52_CR7","doi-asserted-by":"publisher","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864. ACM (2016). https:\/\/doi.org\/10.1145\/2939672.2939754","DOI":"10.1145\/2939672.2939754"},{"issue":"10","key":"52_CR8","first-page":"2954","volume":"30","author":"W Jiehua","year":"2013","unstructured":"Jiehua, W.: Link prediction based on partitioning com-munity and differentiating role of common neighbors. Appl. Res. Comput. 30(10), 2954\u20132957 (2013)","journal-title":"Appl. Res. Comput."},{"key":"52_CR9","unstructured":"Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In: Advances in Neural Information Processing Systems, pp. 539\u2013547 (2012)"},{"issue":"4","key":"52_CR10","doi-asserted-by":"publisher","first-page":"046122","DOI":"10.1103\/PhysRevE.80.046122","volume":"80","author":"L L\u00fc","year":"2009","unstructured":"L\u00fc, L., Jin, C.H., Zhou, T.: Similarity index based on local paths for link prediction of complex networks. Phys. Rev. E 80(4), 046122 (2009)","journal-title":"Phys. Rev. E"},{"issue":"4","key":"52_CR11","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1145\/3012704","volume":"49","author":"V Martinez","year":"2017","unstructured":"Martinez, V., Berzal, F., Cubero, J.: A survey of link prediction in complex networks. ACM Comput. Surv. 49(4), 69 (2017). https:\/\/doi.org\/10.1145\/3012704","journal-title":"ACM Comput. Surv."},{"key":"52_CR12","unstructured":"Mikolov, T., Sutskever, I., Kai, C., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, vol. 26, pp. 3111\u20133119 (2013)"},{"key":"52_CR13","doi-asserted-by":"publisher","unstructured":"Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1105\u20131114. ACM (2016). https:\/\/doi.org\/10.1145\/2939672.2939751","DOI":"10.1145\/2939672.2939751"},{"issue":"2","key":"52_CR14","doi-asserted-by":"publisher","first-page":"021102","DOI":"10.1103\/PhysRevE.75.021102","volume":"75","author":"Q Ou","year":"2007","unstructured":"Ou, Q., Jin, Y.D., Zhou, T., Wang, B.H., Yin, B.Q.: Power-law strength-degree correlation from resource-allocation dynamics on weighted networks. Phys. Rev. E 75(2), 021102 (2007)","journal-title":"Phys. Rev. E"},{"key":"52_CR15","doi-asserted-by":"publisher","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701\u2013710. ACM (2014). https:\/\/doi.org\/10.1145\/2623330.2623732","DOI":"10.1145\/2623330.2623732"},{"key":"52_CR16","doi-asserted-by":"publisher","unstructured":"Ribeiro, L.F., Saverese, P.H., Figueiredo, D.R.: struc2vec: learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 385\u2013394. ACM (2017). https:\/\/doi.org\/10.1145\/3097983.3098061","DOI":"10.1145\/3097983.3098061"},{"key":"52_CR17","doi-asserted-by":"crossref","unstructured":"Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015). http:\/\/networkrepository.com","DOI":"10.1609\/aaai.v29i1.9277"},{"key":"52_CR18","doi-asserted-by":"publisher","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067\u20131077. International World Wide Web Conferences Steering Committee (2015). https:\/\/doi.org\/10.1145\/2736277.2741093","DOI":"10.1145\/2736277.2741093"},{"key":"52_CR19","doi-asserted-by":"publisher","unstructured":"Tang, L., Liu, H.: Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 817\u2013826. ACM (2009). https:\/\/doi.org\/10.1145\/1557019.1557109","DOI":"10.1145\/1557019.1557109"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2019: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29911-8_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T05:02:31Z","timestamp":1664168551000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-29911-8_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030299101","9783030299118"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29911-8_52","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cuvu, Yanuka Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fiji","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}