{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:22:07Z","timestamp":1785543727627,"version":"3.56.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T00:00:00Z","timestamp":1634083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T00:00:00Z","timestamp":1634083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CAREER IIS-1942929"],"award-info":[{"award-number":["CAREER IIS-1942929"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CAREER CMMI-1750531"],"award-info":[{"award-number":["CAREER CMMI-1750531"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ECCS-1609916"],"award-info":[{"award-number":["ECCS-1609916"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s41060-021-00288-8","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T22:05:27Z","timestamp":1634162727000},"page":"33-46","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Graph sparsification with graph convolutional networks"],"prefix":"10.1007","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9789-1908","authenticated-orcid":false,"given":"Jiayu","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengmin","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Makan","family":"Fardad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Zafarani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,13]]},"reference":[{"key":"288_CR1","doi-asserted-by":"crossref","unstructured":"Bencz\u00far, A.A., Karger, D.R.: Approximating s-t minimum cuts in \u00d5(n$${}^{\\text{2}}$$) time. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing, Philadelphia, Pennsylvania, USA, May 22\u201324, 1996, pp. 47\u201355 (1996)","DOI":"10.1145\/237814.237827"},{"key":"288_CR2","doi-asserted-by":"publisher","unstructured":"Bhagat, S., Cormode, G., Muthukrishnan, S.: Node classification in social networks. In: Aggarwal, C.C. (eds.) Social Network Data Analytics, pp. 115\u2013148. Springer, Boston (2011). https:\/\/doi.org\/10.1007\/978-1-4419-8462-3_5","DOI":"10.1007\/978-1-4419-8462-3_5"},{"key":"288_CR3","unstructured":"Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., et\u00a0al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends\u00ae Mach. Learn. 3(1), 1\u2013122 (2011)"},{"key":"288_CR4","doi-asserted-by":"crossref","unstructured":"Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI\u201910, pp. 1306\u20131313. AAAI Press (2010)","DOI":"10.1609\/aaai.v24i1.7519"},{"key":"288_CR5","unstructured":"Chen, J., Ma, T., Xiao, C.: FastGCN: fast learning with graph convolutional networks via importance sampling. In: International Conference on Learning Representations (2018)"},{"key":"288_CR6","unstructured":"Chen, S., Varma, R., Singh, A., Kovacevic, J.: Signal representations on graphs: tools and applications. CoRR (2015). arXiv:1512.05406"},{"key":"288_CR7","doi-asserted-by":"crossref","unstructured":"Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., Hsieh, C.-J.: Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks (2019)","DOI":"10.1145\/3292500.3330925"},{"key":"288_CR8","doi-asserted-by":"crossref","unstructured":"Cho, K., Merrienboer, B.V., Gulcehre, C., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. Comput. Sci. CoRR (2014). arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"288_CR9","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering . In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29. Curran Associates, Inc. (2016). https:\/\/proceedings.neurips.cc\/paper\/2016\/file\/04df4d434d481c5bb723be1b6df1ee65-Paper.pdf"},{"key":"288_CR10","doi-asserted-by":"crossref","unstructured":"Feng, Z.: Spectral graph sparsification in nearly-linear time leveraging efficient spectral perturbation analysis. In: Proceedings of the 53rd Annual Design Automation Conference, DAC \u201916, pp. 57:1\u201357:6. ACM, New York (2016)","DOI":"10.1145\/2897937.2898094"},{"key":"288_CR11","doi-asserted-by":"crossref","unstructured":"Fung, W.S., Hariharan, R., Harvey, N.J., Panigrahi, D.: A general framework for graph sparsification. In: Proceedings of the Forty-Third Annual ACM Symposium on Theory of Computing, STOC \u201911, pp. 71\u201380. ACM, New York (2011)","DOI":"10.1145\/1993636.1993647"},{"key":"288_CR12","doi-asserted-by":"crossref","unstructured":"Geng, X., Zhang, H., Bian, J., Chua, T.: Learning image and user features for recommendation in social networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4274\u20134282 (2015)","DOI":"10.1109\/ICCV.2015.486"},{"key":"288_CR13","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry . In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 1263\u20131272. PMLR (2017). http:\/\/proceedings.mlr.press\/v70\/gilmer17a\/gilmer17a.pdf"},{"key":"288_CR14","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: NIPS (2017)"},{"issue":"1","key":"288_CR15","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1137\/140990309","volume":"26","author":"M Hong","year":"2016","unstructured":"Hong, M., Luo, Z.Q., Razaviyayn, M.: Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems. SIAM J. Optim. 26(1), 337\u2013364 (2016)","journal-title":"SIAM J. Optim."},{"key":"288_CR16","doi-asserted-by":"crossref","unstructured":"Karger, D.R.: Random sampling in cut, flow, and network design problems. In: Proceedings of the Twenty-Sixth Annual ACM Symposium on Theory of Computing, 23\u201325 May 1994, Montr\u00e9al, Qu\u00e9bec, Canada, pp. 648\u2013657 (1994)","DOI":"10.1145\/195058.195422"},{"key":"288_CR17","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks . CoRR (2016). arXiv:1609.02907"},{"key":"288_CR18","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/978-3-030-47426-3_22","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"J Li","year":"2020","unstructured":"Li, J., Zhang, T., Tian, H., Jin, S., Fardad, M., Zafarani, R.: SGCN: a graph sparsifier based on graph convolutional networks. In: Lauw, H.W., Wong, R.C.W., Ntoulas, A., Lim, E.P., Ng, S.K., Pan, S.J. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 275\u2013287. Springer, Cham (2020)"},{"key":"288_CR19","doi-asserted-by":"crossref","unstructured":"Lindner, G., Staudt, C.L., Hamann, M., Meyerhenke, H., Wagner, D.: Structure-preserving sparsification of social networks. In: Proceedings of the 2015 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM \u201915, pp. 448\u2013454. ACM, New York (2015)","DOI":"10.1145\/2808797.2809313"},{"issue":"6","key":"288_CR20","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1016\/j.physa.2010.11.027","volume":"390","author":"L L\u00fc","year":"2011","unstructured":"L\u00fc, L., Zhou, T.: Link prediction in complex networks: a survey. Physica A Stat. Mech. Appl. 390(6), 1150\u20131170 (2011)","journal-title":"Physica A Stat. Mech. Appl."},{"key":"288_CR21","unstructured":"Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs . In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of the 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 2014\u20132023. PMLR, New York (2016). http:\/\/proceedings.mlr.press\/v48\/niepert16.pdf"},{"key":"288_CR22","doi-asserted-by":"crossref","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, KDD \u201914, pp. 701\u2013710. ACM, New York (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"288_CR23","unstructured":"Rong, Y., Huang, W., Xu, T., Huang, J.: Dropedge: towards deep graph convolutional networks on node classification. In: International Conference on Learning Representations (2020)"},{"key":"288_CR24","doi-asserted-by":"crossref","unstructured":"Satuluri, V., Parthasarathy, S., Ruan, Y.: Local graph sparsification for scalable clustering. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, SIGMOD \u201911, pp. 721\u2013732. ACM, New York (2011)","DOI":"10.1145\/1989323.1989399"},{"issue":"1","key":"288_CR25","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. Trans. Neural Netw. 20(1), 61\u201380 (2009)","journal-title":"Trans. Neural Netw."},{"issue":"1","key":"288_CR26","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.cosrev.2007.05.001","volume":"1","author":"SE Schaeffer","year":"2007","unstructured":"Schaeffer, S.E.: Survey: graph clustering. Comput. Sci. Rev. 1(1), 27\u201364 (2007)","journal-title":"Comput. Sci. Rev."},{"issue":"3","key":"288_CR27","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen, P., Namata, G.M., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93\u2013106 (2008)","journal-title":"AI Mag."},{"issue":"16","key":"288_CR28","doi-asserted-by":"publisher","first-page":"6483","DOI":"10.1073\/pnas.0808904106","volume":"106","author":"M\u00c1 Serrano","year":"2009","unstructured":"Serrano, M.\u00c1., Bogu\u00f1\u00e1, M., Vespignani, A.: Extracting the multiscale backbone of complex weighted networks. Proc. Natl. Acad. Sci. U. S. A. 106(16), 6483\u20138 (2009)","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"288_CR29","doi-asserted-by":"crossref","unstructured":"Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: Signal processing on graphs: extending high-dimensional data analysis to networks and other irregular data domains . CoRR (2012). arXiv:1211.0053","DOI":"10.1109\/MSP.2012.2235192"},{"key":"288_CR30","doi-asserted-by":"crossref","unstructured":"Spielman, D.A., Srivastava, N.: Graph sparsification by effective resistances. In: Proceedings of the Fortieth Annual ACM Symposium on Theory of Computing. STOC\u201908, Victoria, British Columbia, Canada, pp. 563\u2013568. Association for Computing Machinery, New York (2008). https:\/\/doi.org\/10.1145\/1374376.1374456","DOI":"10.1145\/1374376.1374456"},{"key":"288_CR31","doi-asserted-by":"crossref","unstructured":"Spielman, D.A., Teng, S.: Nearly linear time algorithms for preconditioning and solving symmetric, diagonally dominant linear systems. SIAM J.Matrix Anal. Appl. 35(3), 835\u2013885 (2014). https:\/\/doi.org\/10.1137\/090771430","DOI":"10.1137\/090771430"},{"key":"288_CR32","doi-asserted-by":"publisher","unstructured":"Takapoui, R., Moehle, N., Boyd, S., Bemporad, A.: A simple effective heuristic for embedded mixed-integer quadratic programming. In: 2016 American Control Conference (ACC), pp. 5619\u20135625 (2016). https:\/\/doi.org\/10.1109\/ACC.2016.7526551","DOI":"10.1109\/ACC.2016.7526551"},{"key":"288_CR33","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)"},{"key":"288_CR34","unstructured":"Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol.\u00a097, pp. 6861\u20136871. PMLR, Long Beach (2019)"},{"key":"288_CR35","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2019). https:\/\/doi.org\/10.1109\/TNNlS.2020.2978386","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"288_CR36","unstructured":"Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, vol. 48, ICML\u201916, pp. 40\u201348. JMLR.org (2016)"},{"key":"288_CR37","unstructured":"Zheng, C., Zong, B., Cheng, W., Song, D., Ni, J., Yu, W., Chen, H., Wang, W.: Robust graph representation learning via neural sparsification. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 119, pp. 11458\u201311468. PMLR (2020)"},{"key":"288_CR38","unstructured":"Zhu, X.: Semi-supervised learning with graphs. Ph.D. Thesis, Pittsburgh, PA, USA (2005). AAI3179046"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-021-00288-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-021-00288-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-021-00288-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T04:11:27Z","timestamp":1673496687000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-021-00288-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,13]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["288"],"URL":"https:\/\/doi.org\/10.1007\/s41060-021-00288-8","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,13]]},"assertion":[{"value":"2 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}