{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:52:01Z","timestamp":1750308721874,"version":"3.41.0"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2013,6,1]],"date-time":"2013-06-01T00:00:00Z","timestamp":1370044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["23500194"],"award-info":[{"award-number":["23500194"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["AOARD-11-4111"],"award-info":[{"award-number":["AOARD-11-4111"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2013,6]]},"abstract":"<jats:p>We addressed the problem of detecting the change in behavior of information diffusion over a social network which is caused by an unknown external situation change using a small amount of observation data in a retrospective setting. The unknown change is assumed effectively reflected in changes in the parameter values in the probabilistic information diffusion model, and the problem is reduced to detecting where in time and how long this change persisted and how big this change is. We solved this problem by searching the change pattern that maximizes the likelihood of generating the observed information diffusion sequences, and in doing so we devised a very efficient general iterative search algorithm using the derivative of the likelihood which avoids parameter value optimization during each search step. This is in contrast to the naive learning algorithm in that it has to iteratively update the patten boundaries, each requiring the parameter value optimization and thus is very inefficient. We tested this algorithm for two instances of the probabilistic information diffusion model which has different characteristics. One is of information push style and the other is of information pull style. We chose Asynchronous Independent Cascade (AsIC) model as the former and Value-weighted Voter (VwV) model as the latter. The AsIC is the model for general information diffusion with binary states and the parameter to detect its change is diffusion probability and the VwV is the model for opinion formation with multiple states and the parameter to detect its change is opinion value. The results tested on these two models using four real-world network structures confirm that the algorithm is robust enough and can efficiently identify the correct change pattern of the parameter values. Comparison with the naive method that finds the best combination of change boundaries by an exhaustive search through a set of randomly selected boundary candidates shows that the proposed algorithm far outperforms the native method both in terms of accuracy and computation time.<\/jats:p>","DOI":"10.1145\/2483669.2483688","type":"journal-article","created":{"date-parts":[[2013,7,1]],"date-time":"2013-07-01T12:27:28Z","timestamp":1372681648000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Detecting changes in information diffusion patterns over social networks"],"prefix":"10.1145","volume":"4","author":[{"given":"Kazumi","family":"Saito","sequence":"first","affiliation":[{"name":"University of Shizuoka, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiro","family":"Kimura","sequence":"additional","affiliation":[{"name":"Ryukoku University, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kouzou","family":"Ohara","sequence":"additional","affiliation":[{"name":"Aoyama Gakuin University, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroshi","family":"Motoda","sequence":"additional","affiliation":[{"name":"Osaka University, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2013,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.80.041129"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835934"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.118"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401914"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2005.16"},{"volume-title":"Proceedings of the 3rd International Conference on Internet and Network Economics. 281--286","author":"Even-Dar E.","key":"e_1_2_1_6_1","unstructured":"Even-Dar , E. and Shapria , A . 2007. A note on maximizing the spread of influence in social networks . In Proceedings of the 3rd International Conference on Internet and Network Economics. 281--286 . Even-Dar, E. and Shapria, A. 2007. A note on maximizing the spread of influence in social networks. In Proceedings of the 3rd International Conference on Internet and Network Economics. 281--286."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011122126881"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835933"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1046456.1046462"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.74.056108"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"volume-title":"Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI'08)","author":"Kimura M.","key":"e_1_2_1_12_1","unstructured":"Kimura , M. , Saito , K. , and Motoda , H . 2008. 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Learning to predict opinion share in social networks. In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10). 1364--1370."},{"key":"e_1_2_1_16_1","first-page":"150","article-title":"Detecting anti-majority opinionists using value-weighted mixture voter model. In Proceedings of the 14th International Conference on Discovery Science (DS'11)","volume":"6926","author":"Kimura M.","year":"2011","unstructured":"Kimura , M. , Saito , K. , Ohara , K. , and Motoda , H. 2011 . Detecting anti-majority opinionists using value-weighted mixture voter model. In Proceedings of the 14th International Conference on Discovery Science (DS'11) . Lecture Notes in Artificial Intelligence , vol. 6926 , Spring er, 150 -- 164 . Kimura, M., Saito, K., Ohara, K., and Motoda, H. 2011. Detecting anti-majority opinionists using value-weighted mixture voter model. In Proceedings of the 14th International Conference on Discovery Science (DS'11). 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