{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:21:40Z","timestamp":1785543700365,"version":"3.56.0"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2017,5]]},"abstract":"<jats:p>\n            Influence maximization is a combinatorial optimization problem that finds important applications in viral marketing, feed recommendation, etc. Recent research has led to a number of scalable approximation algorithms for influence maximization, such as\n            <jats:italic>\n              TIM\n              <jats:sup>+<\/jats:sup>\n            <\/jats:italic>\n            and\n            <jats:italic>IMM<\/jats:italic>\n            , and more recently,\n            <jats:italic>SSA<\/jats:italic>\n            and\n            <jats:italic>D-SSA<\/jats:italic>\n            . The goal of this paper is to conduct a rigorous theoretical and experimental analysis of\n            <jats:italic>SSA<\/jats:italic>\n            and\n            <jats:italic>D-SSA<\/jats:italic>\n            and compare them against the preceding algorithms. In doing so, we uncover inaccuracies in previously reported technical results on the accuracy and efficiency of\n            <jats:italic>SSA<\/jats:italic>\n            and\n            <jats:italic>D-SSA<\/jats:italic>\n            , which we set right. We also attempt to reproduce the original experiments on\n            <jats:italic>SSA<\/jats:italic>\n            and\n            <jats:italic>D-SSA<\/jats:italic>\n            , based on which we provide interesting empirical insights. Our evaluation confirms some results reported from the original experiments, but it also reveals anomalies in some other results and sheds light on the behavior of\n            <jats:italic>SSA<\/jats:italic>\n            and\n            <jats:italic>D-SSA<\/jats:italic>\n            in some important settings not considered previously. We also report on the performance of\n            <jats:italic>SSA-Fix<\/jats:italic>\n            , our modification to\n            <jats:italic>SSA<\/jats:italic>\n            in order to restore the approximation guarantee that was claimed for but not enjoyed by\n            <jats:italic>SSA<\/jats:italic>\n            . Overall, our study suggests that there exist opportunities for further scaling up influence maximization with approximation guarantees.\n          <\/jats:p>","DOI":"10.14778\/3099622.3099623","type":"journal-article","created":{"date-parts":[[2017,9,7]],"date-time":"2017-09-07T13:35:53Z","timestamp":1504791353000},"page":"913-924","source":"Crossref","is-referenced-by-count":110,"title":["Revisiting the stop-and-stare algorithms for influence maximization"],"prefix":"10.14778","volume":"10","author":[{"given":"Keke","family":"Huang","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sibo","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Glenn","family":"Bevilacqua","sequence":"additional","affiliation":[{"name":"University of British Columbia, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaokui","family":"Xiao","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laks V. S.","family":"Lakshmanan","sequence":"additional","affiliation":[{"name":"University of British Columbia, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,5]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"https:\/\/sites.google.com\/site\/vldb2017imexptr\/.  https:\/\/sites.google.com\/site\/vldb2017imexptr\/."},{"key":"e_1_2_1_2_1","unstructured":"https:\/\/sourceforge.net\/projects\/im-imm\/.  https:\/\/sourceforge.net\/projects\/im-imm\/."},{"key":"e_1_2_1_3_1","unstructured":"https:\/\/github.com\/hungnt55\/Stop-and-Stare.  https:\/\/github.com\/hungnt55\/Stop-and-Stare."},{"key":"e_1_2_1_4_1","first-page":"306","volume-title":"WINE","author":"Bharathi S.","year":"2007","unstructured":"S. Bharathi , D. Kempe , and M. Salek . Competitive influence maximization in social networks . In WINE , pages 306 -- 311 , 2007 . S. Bharathi, D. Kempe, and M. Salek. Competitive influence maximization in social networks. 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Sch\u00f6lkopf . Influence maximization in continuous time diffusion networks . In ICML , 2012 . M. Gomez-Rodriguez and B. Sch\u00f6lkopf. Influence maximization in continuous time diffusion networks. In ICML, 2012."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/2047485.2047492"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1963192.1963217"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.79"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/11523468_91"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2013.6544831"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281239"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2915207"},{"key":"e_1_2_1_22_1","volume-title":"Sep 7","author":"Nguyen H. 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