本文關鍵詞：基于NSGA2的網絡環(huán)境下多標簽種子節(jié)點選擇　出處：《電子與信息學報》2017年09期 　論文類型：期刊論文

  更多相關文章： 社交網絡 多標簽分類 NSGA 系統(tǒng)開銷

【摘要】：隨著社交網絡規(guī)模的不斷擴大,網絡節(jié)點的標簽分類也不再單一,變得豐富多樣,這些促使了社交網絡中的多標簽分類問題成為一個重要的研究領域。以前的研究重點主要集中在提高預測網絡節(jié)點標簽的精度上,而忽略了得到節(jié)點信息所產生的包含時間消耗和計算資源等在內的系統(tǒng)開銷問題�？涩F(xiàn)如今隨著網絡規(guī)模不斷擴大且復雜性不斷增強,之前所忽略的系統(tǒng)開銷問題變得越來越嚴重,增加了預測標簽的成本,加重了預測網絡節(jié)點標簽的難度。該文針對這一問題提出了基于NSGA2算法的網絡環(huán)境下多標簽種子節(jié)點選擇算法(NAMESEA算法),目的是在能大大降低預測節(jié)點標簽所消耗的系統(tǒng)開銷的前提下一定程度上提高預測標簽的精度。該文將NAMESEA算法與其他多標簽預測算法在多個真實數(shù)據(jù)集上進行實驗對比,結果證明NAMESEA算法大大降低了預測節(jié)點標簽的系統(tǒng)開銷并且提高了預測精度。
[Abstract]:With the continuous expansion of the scale of social networks, the label classification of network nodes is no longer single, becoming rich and diverse. This makes the classification of multiple tags in social networks become an important research field. The previous research focuses on improving the accuracy of the prediction network node labels. However, the problem of system overhead caused by getting node information, including time consumption and computing resources, has been neglected. But now, with the increasing of network size and complexity, it is becoming more and more complex. The problem of overhead that was previously ignored has become more and more serious, increasing the cost of forecasting tags. In order to solve this problem, a multi-label seed node selection algorithm based on NSGA2 algorithm is proposed in this paper. The purpose of this paper is to improve the accuracy of prediction tags on the premise of greatly reducing the system overhead consumed by prediction node tags. In this paper, the NAMESEA algorithm and other multi-label prediction algorithms are used in multiple real numbers. An experimental comparison was made on the set. The results show that the NAMESEA algorithm greatly reduces the system overhead and improves the prediction accuracy.
【作者單位】： 合肥工業(yè)大學計算機與信息學院;科學技術部基礎研究管理中心;路易斯安那州立大學計算機與信息學院;
【基金】：國家973規(guī)劃項目(2013CB329604) 國家重點研發(fā)計劃項目(2016YFB1000901) 國家自然科學基金項目(61503114)~~
【分類號】：TP18;TP393.09
【正文快照】： (2)(科學技術部基礎研究管理中心北京100862)(3)(路易斯安那州立大學計算機與信息學院拉斐特70503美國)近年來隨著社交網絡應用的發(fā)展普及,社交網絡吸引了越來越多學者的研究目光[1-5],其中一個重要的研究方向就是社交網絡中的多標簽預測問題[1]。利用標簽預測我們可以通過網，

本文編號：1436843