{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:44:18Z","timestamp":1777704258350,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2018,6,13]],"date-time":"2018-06-13T00:00:00Z","timestamp":1528848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,10]]},"abstract":"<jats:p>In this paper, the prediction of damage results for complex network is considered under grey information attack. Firstly, in order to construct more realistic networks, a new algorithm is proposed to generate 3 types of fully connected networks (normal scale-free network, scale-free network with cutoff, random network). Secondly, robustness of the 3 networks is analyzed under grey information attack. And then, a new method is proposed to predict the damage results by training the BP neural network. Thirdly, the effects of different topological parameters on the damage results are analyzed and a new method is proposed to find central nodes of the network. Finally, the damage results of a real bus network under grey information attack are predicted by the proposed method and several suggestions are given to help protect the real bus network.<\/jats:p>","DOI":"10.3233\/jifs-17121","type":"journal-article","created":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T13:30:46Z","timestamp":1529069446000},"page":"3147-3162","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of damage results of complex network under grey information attack"],"prefix":"10.1177","volume":"35","author":[{"given":"Tao","family":"Ren","sequence":"first","affiliation":[{"name":"Software College, Northeastern University, Shenyang, P.R. China"}]},{"given":"Miao-Miao","family":"Liu","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang, P.R. 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