Search Results - "Lv, Laishui"

Evolutionary Perturbation Attack on Temporal Link Prediction by Zhang, Ting, Lv, Laishui, Bardou, Dalal

“…Link prediction plays an important role in network analysis and can be applied to many applications, including marketing analysis and information retrieval…”
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Eigenvector Centrality Based on Inter-layer Similarity for Link Prediction in Temporal Network by Zhang, Ting, Zhang, Kun, Lv, Laishui, Li, Xun, Cai, Ying

“…Link prediction in temporal networks is an important task with various applications. Dealing with this task is more sophisticated than in static networks,…”
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Graph Regularized Non-negative Matrix Factorization for Temporal Link Prediction Based on Communicability by Zhang, Ting, Zhang, Kun, Lv, Laishui, Bardou, Dalal

“…Temporal link prediction is a fundamental research problem in network analysis and has gained increasing attention in recent years. In order to improve the…”
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A New Centrality Measure Based on Topologically Biased Random Walks for Multilayer Networks by Lv, Laishui, Zhang, Kun, Bardou, Dalal, Zhang, Ting, Cai, Ying

“…In this paper, we propose a new multirank method based on topologically biased random walks for simultaneously ranking the nodes and layers in multilayer…”
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A new centrality measure based on random walks for multilayer networks under the framework of tensor computation by Lv, Laishui, Zhang, Kun, Bardou, Dalal, Zhang, Ting, Zhang, Jiahui, Cai, Ying, Jiang, Tongtong

“…In this paper, we introduce a fourth-order tensor to represent multilayer networks and establish a new centrality for identifying essential nodes based on…”
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Eigenvector Centrality Measure Based on Node Similarity for Multilayer and Temporal Networks by Lv, Laishui, Zhang, Kun, Zhang, Ting, Li, Xun, Zhang, Jiahui, Xue, Wei

“…Centrality of nodes is very useful for understanding the behavior of systems and has recently attracted plenty of attention from researchers. In this paper, we…”
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Adversarial nonnegative matrix factorization for temporal link prediction by Zhang, Ting, Lv, Laishui, Bardou, Dalal

“…Temporal link prediction has been extensively studied and widely applied in various applications, aiming to predict future network links based on the…”
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Graph regularized nonnegative matrix factorization for link prediction in directed temporal networks using PageRank centrality by Lv, Laishui, Bardou, Dalal, Hu, Peng, Liu, Yanqiu, Yu, Gaohang

“…Temporal link prediction aims to predict new links at time T + 1 based on a given temporal network from time 1 to T. Some existing temporal link prediction…”
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Deep Autoencoder-like non-negative matrix factorization with graph regularized for link prediction in dynamic networks by Lv, Laishui, Bardou, Dalal, Liu, Yanqiu, Hu, Peng

“…Link prediction in dynamic(temporal) networks refers to predicting future edges by analyzing the available network information. Among the existing temporal…”
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An improved gravity centrality for finding important nodes in multi-layer networks based on multi-PageRank by Lv, Laishui, Zhang, Ting, Hu, Peng, Bardou, Dalal, Niu, Shanzhou, Zheng, Zijun, Yu, Gaohang, Wu, Heng

“…How to identify important nodes in multi-layer networks is still an unresolved issue in network science, which has aroused the interest of many researchers. In…”
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Hair removal in dermoscopy images using variational autoencoders by Bardou, Dalal, Bouaziz, Hamida, Lv, Laishui, Zhang, Ting

“…Background In recent years, melanoma is rising at a faster rate compared to other cancers. Although it is the most serious type of skin cancer, the diagnosis…”
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Semi-supervised link prediction based on non-negative matrix factorization for temporal networks by Zhang, Ting, Zhang, Kun, Li, Xun, Lv, Laishui, Sun, Qi

“…•Using graph communicability to represent the time-evolving patterns of temporal networks.•Employing the community information as priors to reflect the…”
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Eigenvector-based centralities for multilayer temporal networks under the framework of tensor computation by Lv, Laishui, Zhang, Kun, Zhang, Ting, Li, Xun, Sun, Qi, Zhang, Lilinqing, Xue, Wei

“…•Using sixth-order tensor to represent multilayer temporal networks.•Cosine similarity is proposed to measure interactions between different layers.•Multilayer…”
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Dual-Channel and Hierarchical Graph Convolutional Networks for document-level relation extraction by Sun, Qi, Xu, Tiancheng, Zhang, Kun, Huang, Kun, Lv, Laishui, Li, Xun, Zhang, Ting, Dore-Natteh, Doris

“…Document-level relation extraction aims to infer complex semantic relations among entities in an entire document. Compared with the sentence-level relation…”
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PageRank centrality for temporal networks by Lv, Laishui, Zhang, Kun, Zhang, Ting, Bardou, Dalal, Zhang, Jiahui, Cai, Ying

“…In this paper, we propose a new centrality measure for ranking the nodes and time layers of temporal networks simultaneously, referred to as the f-PageRank…”
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ICCL: Independent and Correlative Correspondence Learning for few-shot image classification by Zheng, Zijun, Wu, Heng, Lv, Laishui, Ye, Hailiang, Zhang, Changchun, Yu, Gaohang

“…Few-shot learning, which aims to transfer knowledge from past experiences to recognize novel categories with limited samples, is a challenging task in computer…”
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An efficient and secure data collection scheme for predictive maintenance of vehicles by Hu, Peng, Chu, Xixi, Lv, Laishui, Zuo, Kaizhong, Ni, Tianjiao, Wang, Taochun, Shen, Zhangyi

“…Through the real-time evaluation of vehicle operating status, the predictive maintenance operated by cloud servers can detect the abnormal condition of…”
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Joint extraction of entities and overlapping relations by improved graph convolutional networks by Sun, Qi, Zhang, Kun, Lv, Laishui, Li, Xun, Huang, Kun, Zhang, Ting

“…Joint extraction of entities and relations is to recognize entities and semantic relations simultaneously, which is significant for knowledge graph…”
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Co-Ranking for nodes, layers and timestamps in multilayer temporal networks by Zhang, Ting, Zhang, Kun, Lv, Laishui, Bardou, Dalal

“…•The mathematical formulation of the six-order tensor is used to represent the multilayer temporal networks.•Three centrality metrics are applied to quantify…”
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Multi-graph aggregated graph neural network for heterogeneous graph representation learning by Zhu, Shuailei, Wang, Xiaofeng, Lai, Shuaiming, Chen, Yuntao, Zhai, Wenchao, Quan, Daying, Qi, Yuanyuan, Lv, Laishui

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