【摘要】：針對(duì)僅考慮網(wǎng)絡(luò)結(jié)構(gòu)來對(duì)異質(zhì)信息網(wǎng)絡(luò)進(jìn)行異常點(diǎn)發(fā)現(xiàn)可能帶來的結(jié)果失真、難以理解等問題,提出一種富屬性異質(zhì)信息網(wǎng)絡(luò)的可約束異常檢測(cè)算法.通過將信息豐富的交互數(shù)據(jù)建模成富屬性異質(zhì)信息網(wǎng)絡(luò),以帶屬性元路徑來指定用戶感興趣的屬性和子空間,綜合網(wǎng)絡(luò)結(jié)構(gòu)和屬性內(nèi)容兩方面來評(píng)估節(jié)點(diǎn)的異常度,給出了可約束的異常檢測(cè)算法框架.在Arxiv真實(shí)數(shù)據(jù)集上進(jìn)行了實(shí)驗(yàn),以帶屬性元路徑來指定對(duì)作者、論文及論文的標(biāo)題和摘要等方面的約束,對(duì)多個(gè)查詢輸出了異常度從高到低的節(jié)點(diǎn)列表及約束域異常點(diǎn)集合.結(jié)果表明:相比僅考慮網(wǎng)絡(luò)結(jié)構(gòu)或僅考慮屬性內(nèi)容的基準(zhǔn)算法,平均準(zhǔn)確率提高12.95%以上.
[Abstract]:In this paper, a constrained anomaly detection algorithm for heterogeneous information networks with rich attributes is proposed, aiming at the distortion and difficulty of finding outliers in heterogeneous information networks considering only the network structure. By modeling the information-rich interactive data into an attribute-rich heterogeneous information network, using attribute element paths to specify the attributes and subspaces of interest to the user, the anomaly degree of the nodes is evaluated by integrating the network structure and attribute content. The framework of constrained anomaly detection algorithm is presented. Experiments are carried out on Arxiv real data sets. Constraints on authors, papers, titles and abstracts are specified with attribute element paths, and outliers from high to low in node list and constraint domain outliers are outputted for multiple queries. The results show that the average accuracy of the benchmark algorithm is 12.95% higher than that of the benchmark algorithm which only considers the network structure or the content of attributes.
【作者單位】： 武漢理工大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;武漢理工大學(xué)交通物聯(lián)網(wǎng)技術(shù)湖北省重點(diǎn)實(shí)驗(yàn)室;武漢理工大學(xué)內(nèi)河航運(yùn)技術(shù)湖北省重點(diǎn)實(shí)驗(yàn)室;華中科技大學(xué)電子信息與通信學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61572219,61502192,61671216,61471408,51479157,51679182) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目(WUT:2016Ⅲ028) 內(nèi)河航運(yùn)技術(shù)湖北省重點(diǎn)實(shí)驗(yàn)室基金資助項(xiàng)目(NHHY2015005)
【分類號(hào)】：O157.5;TP301.6

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