{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:21:43Z","timestamp":1783023703008,"version":"3.54.6"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819219254","type":"print"},{"value":"9789819219261","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-1926-1_24","type":"book-chapter","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T15:50:31Z","timestamp":1782748231000},"page":"296-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fair Adaptive Influence Maximization in\u00a0Social Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1149-348X","authenticated-orcid":false,"given":"Yi","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0673-2267","authenticated-orcid":false,"given":"Wenyue","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7661-3917","authenticated-orcid":false,"given":"Yanhao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9646-291X","authenticated-orcid":false,"given":"Yuchen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0509-9129","authenticated-orcid":false,"given":"Panagiotis","family":"Karras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1613\/jair.1.13367","volume":"73","author":"R Becker","year":"2022","unstructured":"Becker, R., D\u2019Angelo, G., Ghobadi, S., Gilbert, H.: Fairness in influence maximization through randomization. J. Artif. Intell. Res. 73, 1251\u20131283 (2022)","journal-title":"J. Artif. Intell. Res."},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Borgs, C., Brautbar, M., Chayes, J.T., Lucier, B.: Maximizing social influence in nearly optimal time. In: SODA. pp. 946\u2013957 (2014)","DOI":"10.1137\/1.9781611973402.70"},{"key":"24_CR3","doi-asserted-by":"publisher","first-page":"10380","DOI":"10.52202\/079017-0332","volume":"37","author":"S Chowdhary","year":"2024","unstructured":"Chowdhary, S., De Pasquale, G., Lanzetti, N., Stoica, A.A., D\u00f6rfler, F.: Fairness in social influence maximization via optimal transport. Adva. Neural Inf. Process. Syst. 37, 10380\u201310413 (2024)","journal-title":"Adva. Neural Inf. Process. Syst."},{"issue":"10","key":"24_CR4","doi-asserted-by":"publisher","first-page":"10583","DOI":"10.1109\/TKDE.2023.3265598","volume":"35","author":"Y Dong","year":"2023","unstructured":"Dong, Y., Ma, J., Wang, S., Chen, C., Li, J.: Fairness in graph mining: A survey. IEEE Trans. Knowl. Data Eng. 35(10), 10583\u201310602 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Farnadi, G., Babaki, B., Gendreau, M.: A unifying framework for fairness-aware influence maximization. In: WWW (Companion). pp. 714\u2013722 (2020)","DOI":"10.1145\/3366424.3383555"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Feng, Y., Patel, A., Cautis, B., Vahabi, H.: Influence maximization with fairness at scale. In: KDD. pp. 4046\u20134055 (2023)","DOI":"10.1145\/3580305.3599847"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Fish, B., Bashardoust, A., Boyd, D., Friedler, S.A., Scheidegger, C., Venkatasubramanian, S.: Gaps in information access in social networks? In: WWW. pp. 480\u2013490 (2019)","DOI":"10.1145\/3308558.3313680"},{"key":"24_CR8","first-page":"427","volume":"42","author":"D Golovin","year":"2011","unstructured":"Golovin, D., Krause, A.: Adaptive submodularity: Theory and applications in active learning and stochastic optimization. J. Artif. Intell. Res. 42, 427\u2013486 (2011)","journal-title":"J. Artif. Intell. Res."},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Guo, Q., Feng, C., Zhang, F., Wang, S.: Efficient algorithm for budgeted adaptive influence maximization: An incremental RR-set update approach. Proc. ACM Manag. Data 1(3), 207:1\u2013207:26 (2023)","DOI":"10.1145\/3617328"},{"issue":"9","key":"24_CR10","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.14778\/3213880.3213883","volume":"11","author":"K Han","year":"2018","unstructured":"Han, K., Huang, K., Xiao, X., Tang, J., Sun, A., Tang, X.: Efficient algorithms for adaptive influence maximization. Proc. VLDB Endow. 11(9), 1029\u20131040 (2018)","journal-title":"Proc. VLDB Endow."},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Huang, K., Tang, J., Han, K., Xiao, X., Chen, W., Sun, A., Tang, X., Lim, A.: Efficient approximation algorithms for adaptive influence maximization. VLDB J. 29(6), 1385\u20131406 (2020)","DOI":"10.1007\/s00778-020-00615-8"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Kempe, D., Kleinberg, J.M., \u00c9va Tardos: Maximizing the spread of influence through a social network. In: KDD. pp. 137\u2013146 (2003)","DOI":"10.1145\/956750.956769"},{"issue":"93","key":"24_CR13","first-page":"2761","volume":"9","author":"A Krause","year":"2008","unstructured":"Krause, A., McMahan, H.B., Guestrin, C., Gupta, A.: Robust submodular observation selection. J. Mach. Learn. Res. 9(93), 2761\u20132801 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR14","unstructured":"Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection. http:\/\/snap.stanford.edu\/data (2014)"},{"issue":"10","key":"24_CR15","doi-asserted-by":"publisher","first-page":"1852","DOI":"10.1109\/TKDE.2018.2807843","volume":"30","author":"Y Li","year":"2018","unstructured":"Li, Y., Fan, J., Wang, Y., Tan, K.L.: Influence maximization on social graphs: A survey. IEEE Trans. Knowl. Data Eng. 30(10), 1852\u20131872 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"24_CR16","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1613\/jair.1.14450","volume":"76","author":"M Lin","year":"2023","unstructured":"Lin, M., Sun, L., Yang, R., Liu, X., Wang, Y., Li, D., Li, W., Lu, S.: Fair influence maximization in large-scale social networks based on attribute-aware reverse influence sampling. J. Artif. Intell. Res. 76, 925\u2013957 (2023)","journal-title":"J. Artif. Intell. Res."},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Ma, W., Egger, M.K., Pavlogiannis, A., Li, Y., Karras, P.: Reachability-aware fair influence maximization. In: APWeb-WAIM (III). pp. 342\u2013359 (2024)","DOI":"10.1007\/978-981-97-7238-4_22"},{"key":"24_CR18","first-page":"5575","volume":"32","author":"B Peng","year":"2019","unstructured":"Peng, B., Chen, W.: Adaptive influence maximization with myopic feedback. Adv. Neural. Inf. Process. Syst. 32, 5575\u20135584 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Rahmattalabi, A., Jabbari, S., Lakkaraju, H., Vayanos, P., Izenberg, M., Brown, R., Rice, E., Tambe, M.: Fair influence maximization: A welfare optimization approach. In: AAAI. pp. 11630\u201311638 (2021)","DOI":"10.1609\/aaai.v35i13.17383"},{"issue":"7","key":"24_CR20","first-page":"3881","volume":"37","author":"X Rui","year":"2025","unstructured":"Rui, X., Wang, Z., Peng, H., Chen, W., Yu, P.S.: A scalable algorithm for fair influence maximization with unbiased estimator. IEEE Trans. Knowl. Data Eng. 37(7), 3881\u20133895 (2025)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"24_CR21","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1287\/ijoc.2022.0384","volume":"36","author":"S Tang","year":"2024","unstructured":"Tang, S., Yuan, J.: Group equality in adaptive submodular maximization. INFORMS J. Comput. 36(2), 359\u2013376 (2024)","journal-title":"INFORMS J. Comput."},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: A martingale approach. In: SIGMOD. pp. 1539\u20131554 (2015)","DOI":"10.1145\/2723372.2723734"},{"issue":"1","key":"24_CR23","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TETC.2020.3031057","volume":"10","author":"G Tong","year":"2022","unstructured":"Tong, G., Wang, R.: On adaptive influence maximization under general feedback models. IEEE Trans. Emerg. Top. Comput. 10(1), 463\u2013475 (2022)","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Tsang, A., Wilder, B., Rice, E., Tambe, M., Zick, Y.: Group-fairness in influence maximization. In: IJCAI. pp. 5997\u20136005 (2019)","DOI":"10.24963\/ijcai.2019\/831"},{"key":"24_CR25","first-page":"9513","volume":"31","author":"R Udwani","year":"2018","unstructured":"Udwani, R.: Multi-objective maximization of monotone submodular functions with cardinality constraint. Adv. Neural. Inf. Process. Syst. 31, 9513\u20139524 (2018)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"24_CR26","unstructured":"Vaswani, S., Lakshmanan, L.V.S.: Adaptive influence maximization in social networks: Why commit when you can adapt? arXiv:1604.08171 (2016)"},{"key":"24_CR27","doi-asserted-by":"crossref","unstructured":"Yuan, J., Tang, S.: No time to observe: Adaptive influence maximization with partial feedback. In: IJCAI. pp. 3908\u20133914 (2017)","DOI":"10.24963\/ijcai.2017\/546"}],"container-title":["Lecture Notes in Computer Science","Data Science: Foundations and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-1926-1_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:44:21Z","timestamp":1783021461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-1926-1_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"ISBN":["9789819219254","9789819219261"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-1926-1_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"30 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}