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In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods. 相似文献
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Egils Avots Klvs Jermakovs Maie Bachmann Laura Peske Cagri Ozcinar Gholamreza Anbarjafari 《Entropy (Basel, Switzerland)》2022,24(2)
Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression. 相似文献
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本文基于多光谱遥感图像,提出了一种融合光谱特征和空间信息的主溜特征提取方法.将遥感影像的两大表现形式--光谱和空间信息结合,利用像元及其邻域的关系来描述其空间结构,用方向线来描述黄河主溜,从而确定其位置和走向.实验结果表明,该方法可有效地提取出黄河主溜特征. 相似文献
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二维主成分分析方法是直接利用二维图像来构建方差矩阵的。为了充分利用样本类别信息,文章以类间散布矩阵特征向量作为投影方向进行特征抽取。首先用2DPCA先作一次横向压缩,对抽取出的特征矩阵再用2DPCA作一次纵向压缩。与传统二维主成分算法比较,极大压缩了特征的维数,加快了分类速度,提高了识别率。用ORL人脸数据库进行了实验验证,证明了本方法的可行性。 相似文献