{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:01:09Z","timestamp":1773511269324,"version":"3.50.1"},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T00:00:00Z","timestamp":1552435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Fundamental Research Funds for the Central University, and the Research Funds of Renmin University of China","award":["2015030273"],"award-info":[{"award-number":["2015030273"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["11671400 and 61672524"],"award-info":[{"award-number":["11671400 and 61672524"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1747818"],"award-info":[{"award-number":["1747818"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2019,4,30]]},"abstract":"<jats:p>\n            In recent years, social networks have become important platforms for people to disseminate information. However, we need to take effective measures such as blocking a set of links to control the negative rumors spreading over the network. In this article, we propose a\n            <jats:italic>Rumor Spread Minimization<\/jats:italic>\n            (RSM) problem, i.e., we remove an edge set from network such that the rumor spread is minimized. We first prove the objective function of RSM problem is not submodular. Then, we propose both submodular lower-bound and upper-bound of the objective function. Next, we develop a heuristic algorithm to approximate the objective function. Furthermore, we reformulate our objective function as the DS function (the Difference of Submodular functions). Finally, we conduct experiments on real-world datasets to evaluate our proposed method. The experiment results show that the upper and lower bounds are very close, which indicates the good quality of them. And, the proposed method outperforms the comparison methods.\n          <\/jats:p>","DOI":"10.1145\/3301302","type":"journal-article","created":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T17:11:58Z","timestamp":1552583518000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":69,"title":["Rumor Blocking through Online Link Deletion on Social Networks"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4825-5673","authenticated-orcid":false,"given":"Ruidong","family":"Yan","sequence":"first","affiliation":[{"name":"Renmin University of China, Beijing, China"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"University of Texas at Dallas, Richardson, TX"}]},{"given":"Weili","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Texas at Dallas, Richardson, TX"}]},{"given":"Deying","family":"Li","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}]},{"given":"Yongcai","family":"Wang","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2019,3,13]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Graph Theory and Combinatorics: Proceedings of the Cambridge Combinatorial Conference in Honour of Paul Erd\u00f6s,{Trinity College","author":"Bollob\u00e1s B\u00e9la","year":"1984"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1963405.1963499"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0701175104"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835934"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557047"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.84.056105"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2184319.2184338"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502525"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2013.34"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/3020652.3020697"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the International Conference on Machine Learning. 855--863","author":"Iyer Rishabh","year":"2013"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623704"},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of the 23nd AAAI Conference of Artificial Intelligence. 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