Results 21 to 30 of about 138,699 (301)
Network-based embedding methods for multi-omics data analysis [PDF]
, 2023 The development of high-throughput technologies has resulted in a significant increase in data, opening up new opportunities to study and better understand how biological systems dynamically interact. Network analysis, which is based on graph theory, can
Parvizi, Poorya
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Context Embedding Networks [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Low dimensional embeddings that capture the main variations of interest in collections of data are important for many applications. One way to construct these embeddings is to acquire estimates of similarity from the crowd. However, similarity is a multi-dimensional concept that varies from individual to individual.
Kim, Kun Ho +2 more
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Network representation learning systematic review: Ancestors and current development state
Machine Learning with Applications, 2021 Real-world information networks are increasingly occurring across various disciplines including online social networks and citation networks. These network data are generally characterized by sparseness, nonlinearity and heterogeneity bringing different ...
Amina Amara +2 more
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Network Alignment with Holistic Embeddings [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2021 Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. It plays an important role in various applications ranging from social network analysis to bioinformatic network interactions. However, existing alignment models either cannot handle large-scale graphs or fail to leverage different
Thanh Trung Huynh +6 more
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Research on Service Recommendation Method of Multi-network Hybrid Embed-ding Learning [PDF]
Jisuanji kexue yu tansuo, 2022 The network embedding method can map the network nodes to a low-dimensional vector space and ext-ract the feature information of each node effectively. In the field of service recommendation, some studies show that the introduction of network embedding ...
WANG Xuechun, LYU Shengkai, WU Hao, HE Peng, ZENG Cheng
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Fusion of text and graph information for machine learning problems on networks [PDF]
PeerJ Computer Science, 2021 Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space.
Ilya Makarov +2 more
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A Network Embedding Algorithm Preserving Community Structure Information [PDF]
Jisuanji gongcheng, 2021 Most existing network embedding algorithms only retain the micro-structure information of the network, but ignore the community structure information which is important in networks.In order to incorporate the community structure information into the ...
Lü Shaoqing, ZHAO Xueli, ZHANG Pan, REN Xincheng
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Embedding-aided network dismantling
Physical Review Research, 2023 Optimal percolation concerns the identification of the minimum-cost strategy for the destruction of any extensive connected components in a network. Solutions of such a dismantling problem are important for the design of optimal strategies of disease ...
Saeed Osat +3 more
doaj +1 more source
IEEE Transactions on Image Processing, 2020 Superpixel segmentation is a fundamental computer vision technique that finds application in a multitude of high level computer vision tasks. Most state-of-the-art superpixel segmentation methods are unsupervised in nature and thus cannot fully utilize frequently occurring texture patterns or incorporate multiscale context.
Utkarsh Gaur, B. S. Manjunath
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Service Embedding in IoT Networks [PDF]
IEEE Access, 2020 The Internet of Things is anticipated to participate in the execution of a variety of complex tasks in the near future. IoT objects capable of handling multiple sensing and actuating functions are the cornerstone of smart applications such as smart buildings, smart factories, home automation, and healthcare automation.
Haider Qays Al-Shammari +3 more
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