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Knowl. Discov. Data"],"published-print":{"date-parts":[[2019,2,28]]},"abstract":"<jats:p>\n            Based on the performance of entire social networks, anomaly analysis for evolving social networks generally ignores the otherness of the evolutionary behaviors of different nodes, such that it is difficult to precisely identify the anomalous evolutionary behaviors of nodes (\n            <jats:italic>AEBN<\/jats:italic>\n            ). Assuming that a node's evolutionary behavior that generates and removes edges normally follows stable evolutionary mechanisms, this study focuses on detecting and assessing\n            <jats:italic>AEBN,<\/jats:italic>\n            whose evolutionary mechanisms deviate from their past mechanisms, and proposes a link prediction detection (\n            <jats:italic>LPD<\/jats:italic>\n            ) method and a matrix perturbation assessment (\n            <jats:italic>MPA<\/jats:italic>\n            ) method.\n            <jats:italic>LPD<\/jats:italic>\n            describes a node's evolutionary behavior by fitting its evolutionary mechanism, and designs indexes for edge generation and removal to evaluate the extent to which the evolutionary mechanism of a node's evolutionary behavior can be fitted by a link prediction algorithm. Furthermore, it detects\n            <jats:italic>AEBN<\/jats:italic>\n            by quantifying the differences among behavior vectors that characterize the node's evolutionary behaviors in different periods. In addition,\n            <jats:italic>MPA<\/jats:italic>\n            considers\n            <jats:italic>AEBN<\/jats:italic>\n            as a perturbation of the social network structure, and quantifies the effect of\n            <jats:italic>AEBN<\/jats:italic>\n            on the social network structure based on matrix perturbation analysis. Extensive experiments on eight disparate real-world networks demonstrate that analyzing\n            <jats:italic>AEBN<\/jats:italic>\n            from the perspective of evolutionary mechanisms is important and beneficial.\n          <\/jats:p>","DOI":"10.1145\/3299886","type":"journal-article","created":{"date-parts":[[2019,1,23]],"date-time":"2019-01-23T19:15:02Z","timestamp":1548270902000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["Detecting and Assessing Anomalous Evolutionary Behaviors of Nodes in Evolving Social Networks"],"prefix":"10.1145","volume":"13","author":[{"given":"Huan","family":"Wang","sequence":"first","affiliation":[{"name":"Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Wu","sequence":"additional","affiliation":[{"name":"Macquarie University, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbin","family":"Hu","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Mininglamp Software Systems and Research Institute of Big Knowledge, Hefei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,1,23]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-8733(03)00009-1"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2733380"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.10.036"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2180861.2180866"},{"key":"e_1_2_1_5_1","volume-title":"Army Science Conference.","author":"Akoglu Leman","year":"2008"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0365-y"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3064884"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"M. 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