{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:16:52Z","timestamp":1770754612664,"version":"3.50.0"},"reference-count":29,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Based on real-world academic data, this study aims to use network embedding technology to mining academic relationships, and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>We propose an academic collaborator recommendation model based on attributed network embedding (ACR-ANE), which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes. The non-local neighbors for scholars are defined to capture strong relationships among scholars. A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>1. The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors. 2. It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Research limitations<\/jats:title>\n                    <jats:p>The designed method works for static networks, without taking account of the network dynamics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Practical implications<\/jats:title>\n                    <jats:p>The designed model is embedded in academic collaboration network structure and scholarly attributes, which can be used to help scholars recommend potential collaborators.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>Experiments on two real-world scholarly datasets, Aminer and APS, show that our proposed method performs better than other baselines.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2478\/jdis-2022-0005","type":"journal-article","created":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T02:33:49Z","timestamp":1643855629000},"page":"37-56","source":"Crossref","is-referenced-by-count":8,"title":["Academic Collaborator Recommendation Based on Attributed Network Embedding"],"prefix":"10.2478","volume":"7","author":[{"given":"Ouxia","family":"Du","sequence":"first","affiliation":[{"name":"School of Computer and Information Science , Southwest University , Chongqing , China"}]},{"given":"Ya","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Information Science , Southwest University , Chongqing , China"}]}],"member":"374","published-online":{"date-parts":[[2022,2,3]]},"reference":[{"key":"2026020217384069309_j_jdis-2022-0005_ref_001","doi-asserted-by":"crossref","unstructured":"Aziz, F., Gul, H., Muhammad, I., & Uddin, I. (2020). Link prediction using node information on local paths. Physica A: Statistical Mechanics and Its Applications, 557, 124980. doi:10.1016\/j.physa.2020.124980.","DOI":"10.1016\/j.physa.2020.124980"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_002","doi-asserted-by":"crossref","unstructured":"Barab\u00e1si, A., Jeong, H., N\u00e9da, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and Its Applications, 311(3\u20134), 590\u2013614. doi:10.1016\/s0378-4371(02)00736-7.","DOI":"10.1016\/S0378-4371(02)00736-7"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_003","unstructured":"Blei, D.M., Ng, A.Y., & Jordan, M.I. (2001). Latent dirichlet allocation. In proceedings of Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3\u20138, 2001, Vancouver, British Columbia, Canada."},{"key":"2026020217384069309_j_jdis-2022-0005_ref_004","doi-asserted-by":"crossref","unstructured":"Cen, Y.K., Zou, X., Zhang, J.W., Yang, H.X., Zhou, J.R., & Tang, J. (2019). Representation learning for attributed multiplex heterogeneous network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi:10.1145\/3292500.3330964.","DOI":"10.1145\/3292500.3330964"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_005","doi-asserted-by":"crossref","unstructured":"Chen, Y.K., Zhang, J., Fang, Y.X., Cao, X., & King, I. (2020). Efficient community search over large directed graph: An augmented index-based approach. In Proceedings of the 29th International Joint Conference on Artificial Intelligence. doi:10.24963\/ijcai.2020\/490.","DOI":"10.24963\/ijcai.2020\/490"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_006","doi-asserted-by":"crossref","unstructured":"Dong, Y., Chawla, N.V., & Swami, A. (2017). Metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145\/3097983.3098036.","DOI":"10.1145\/3097983.3098036"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_007","doi-asserted-by":"crossref","unstructured":"Grover, A., & Leskovec, J. (2016). Node2vec. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145\/2939672.2939754.","DOI":"10.1145\/2939672.2939754"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_008","doi-asserted-by":"crossref","unstructured":"Kong, X.J., Jiang, H.Z., Wang, W., Bekele, T.M., Xu, Z.Z., & Wang, M. (2017). Exploring dynamic research interest and academic influence for scientific collaborator recommendation. Scientometrics, 113(1), 369\u2013385. doi:10.1007\/s11192-017-2485-9.","DOI":"10.1007\/s11192-017-2485-9"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_009","doi-asserted-by":"crossref","unstructured":"Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673\u2013702. doi:10.1177\/0306312705052359.","DOI":"10.1177\/0306312705052359"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_010","doi-asserted-by":"crossref","unstructured":"Liu, Z., Xie, X., & Chen, L. (2018). Context-aware academic collaborator recommendation. KDD 2018, 1870\u20131879.","DOI":"10.1145\/3219819.3220050"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_011","doi-asserted-by":"crossref","unstructured":"Lopes, G.R., Moro, M.M., Wives, L.K., & Oliveira, J.P. (2010). Collaboration recommendation on academic social networks. Lecture Notes in Computer Science Advances in Conceptual Modeling\u2014Applications and Challenges, 190\u2013199. doi:10.1007\/978-3-642-16385-2_24.","DOI":"10.1007\/978-3-642-16385-2_24"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_012","doi-asserted-by":"crossref","unstructured":"L\u00fc, L.Y., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications, 390(6), 1150\u20131170. doi:10.1016\/j.physa.2010.11.027.","DOI":"10.1016\/j.physa.2010.11.027"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_013","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). DeepWalk. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145\/2623330.2623732.","DOI":"10.1145\/2623330.2623732"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_014","doi-asserted-by":"crossref","unstructured":"Salakhutdinov, R., & Hinton, G. (2009). Semantic hashing. International Journal of Approximate Reasoning, 50(7), 969\u2013978. doi:10.1016\/j.ijar.2008.11.006.","DOI":"10.1016\/j.ijar.2008.11.006"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_015","doi-asserted-by":"crossref","unstructured":"Shi, C., Hu, B.B., Zhao, W.X., & Yu, P.S. (2019). Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, 31(2), 357\u2013370. doi:10.1109\/TKDE.2018.2833443.","DOI":"10.1109\/TKDE.2018.2833443"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_016","doi-asserted-by":"crossref","unstructured":"Sinatra, R., Wang, D.S., Deville, P., Song, C., & Barab\u00e1si, A. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312). doi:10.1126\/science.aaf5239.","DOI":"10.1126\/science.aaf5239"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_017","doi-asserted-by":"crossref","unstructured":"Sun, X., Yu, Y.B., Liang, Y., Dong, J., Plant, C., & B\u00f6hm, C. (2021). Fusing attributed and topological global-relations for network embedding. Information Sciences, 558, 76\u201390. doi:10.1016\/j.ins.2021.01.012.","DOI":"10.1016\/j.ins.2021.01.012"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_018","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M.Z., Zhang, M., Yan, J., & Mei, Q.Z. (2015). LINE: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web. doi:10.1145\/2736277.2741093.","DOI":"10.1145\/2736277.2741093"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_019","doi-asserted-by":"crossref","unstructured":"Wang, W., Yu, S., Bekele, T.M., Kong, X.J., & Xia, F. (2017). Scientific collaboration patterns vary with scholars\u2019 academic ages. Scientometrics, 112(1), 329\u2013343. doi:10.1007\/s11192-017-2388-9.","DOI":"10.1007\/s11192-017-2388-9"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_020","doi-asserted-by":"crossref","unstructured":"Wang, W., Liu, J.Y., Yang, Z., Kong, X.J., & Xia, F. (2019). Sustainable collaborator recommendation based on conference closure. IEEE Transactions on Computational Social Systems, 6(2), 311\u2013322. doi:10.1109\/tcss.2019.2898198.","DOI":"10.1109\/TCSS.2019.2898198"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_021","doi-asserted-by":"crossref","unstructured":"Wang, W., Liu, J.Y., Tang, T., Tuarob, S., Xia, F., Gong, Z.G., & King, I. (2021). Attributed collaboration network embedding for academic relationship mining. ACM Transactions on the Web, 15(1), 1\u201320. doi:10.1145\/3409736.","DOI":"10.1145\/3409736"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_022","doi-asserted-by":"crossref","unstructured":"Wang, D.X., Cui, P., & Zhu, W.W. (2016). Structural deep network embedding. KDD. 1225\u20131234. doi:http:\/\/dx.doi.org\/10.1145\/2939672.2939753.","DOI":"10.1145\/2939672.2939753"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_023","doi-asserted-by":"crossref","unstructured":"Xia, F., Wang, W., Bekele, T.M., & Liu, H. (2017). Big scholarly data: A Survey. IEEE Transactions on Big Data, 3(1), 18\u201335. doi:10.1109\/tbdata.2016.2641460.","DOI":"10.1109\/TBDATA.2016.2641460"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_024","doi-asserted-by":"crossref","unstructured":"Xia, F., Chen, Z., Wang, W., Li, J., & Yang, L.T. (2014). MVCWalker: Random walk-based most valuable collaborators recommendation exploiting academic factors. IEEE Transactions on Emerging Topics in Computing, 2(3), 364\u2013375. doi:10.1109\/tetc.2014.2356505.","DOI":"10.1109\/TETC.2014.2356505"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_025","unstructured":"Yang, C., Liu, Z.Y., Sun, M.S., Zhao, D.L., & Chang, E. (2015). Network representation learning with rich text information. In Proceedings of the 24th International Conference on Artificial Intelligence. 2111\u20132117."},{"key":"2026020217384069309_j_jdis-2022-0005_ref_026","doi-asserted-by":"crossref","unstructured":"Zhang, C.Y., Wu, X.Q., Yan, W., Wang, L.K., & Zhang, L. (2020). Attribute-aware graph recurrent networks for scholarly friend recommendation based on Internet of scholars in scholarly big data. IEEE Transactions on Industrial Informatics, 16(4), 2707\u20132715. doi:10.1109\/tii.2019.2947066.","DOI":"10.1109\/TII.2019.2947066"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_027","doi-asserted-by":"crossref","unstructured":"Zhang, H.M., Qiu, L.W., Yi, L.L., & Song, Y.Q. (2018). Scalable multiplex network embedding. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. doi:10.24963\/ijcai.2018\/428.","DOI":"10.24963\/ijcai.2018\/428"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_028","doi-asserted-by":"crossref","unstructured":"Zhou, X.K., Liang, W., Wang, K.I., Huang, R.H., & Jin, Q. (2021). Academic influence aware and multidimensional network analysis for research collaboration navigation based on scholarly big data. IEEE Transactions on Emerging Topics in Computing, 9(1), 246\u2013257. doi:10.1109\/tetc.2018.2860051.","DOI":"10.1109\/TETC.2018.2860051"},{"key":"2026020217384069309_j_jdis-2022-0005_ref_029","doi-asserted-by":"crossref","unstructured":"Zhou, X., Ding, L.X., Li, Z.K., & Wan, R.Z. (2017). Collaborator recommendation in heterogeneous bibliographic networks using random walks. Information Retrieval Journal, 20(4), 317\u2013337. doi:10.1007\/s10791-017-9300-3.","DOI":"10.1007\/s10791-017-9300-3"}],"container-title":["Journal of Data and Information Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.2478\/jdis-2022-0005\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.2478\/jdis-2022-0005\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T17:38:53Z","timestamp":1770053933000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.2478\/jdis-2022-0005\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,1]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,2,3]]},"published-print":{"date-parts":[[2022,2,1]]}},"alternative-id":["10.2478\/jdis-2022-0005"],"URL":"https:\/\/doi.org\/10.2478\/jdis-2022-0005","relation":{},"ISSN":["2543-683X"],"issn-type":[{"value":"2543-683X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,1]]}}}