Results 31 to 40 of about 206,969 (259)
Structural Hierarchy-Enhanced Network Representation Learning
Applied Sciences, 2020 Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP).
Cheng-Te Li, Hong-Yu Lin
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Attributed Network Representation Learning Based on Matrix Factorization [PDF]
Jisuanji gongcheng, 2020 To combine the information of network topological structure and node attribute to improve the quality of network representation learning,this paper proposes a new attributed network representation learning algorithm,named ANEMF.The algorithm introduces ...
ZHANG Pan, LU Guangyue, Lü Shaoqing, ZHAO Xueli
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Multi-view learning-based heterogeneous network representation learning
Journal of King Saud University: Computer and Information Sciences, 2023 Network representation learning is an important tool for extracting latent features from heterogeneous networks to enhance downstream analysis tasks. However, for heterogeneous networks in the era of big data, their heterogeneity, unseen network noises ...
Lei Chen, Yuan Li, Xingye Deng
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Learning Vertex Representations for Bipartite Networks [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2022 Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types.
Ming Gao 0001 +5 more
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Temporal network embedding framework with causal anonymous walks representations [PDF]
PeerJ Computer Science, 2022 Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding.
Ilya Makarov +7 more
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An Optimized Network Representation Learning Algorithm Using Multi-Relational Data
Mathematics, 2019 Representation learning aims to encode the relationships of research objects into low-dimensional, compressible, and distributed representation vectors.
Zhonglin Ye +4 more
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On Learning and Learned Data Representation by Capsule Networks [PDF]
IEEE Access, 2019 In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks.
Ancheng Lin, Jun Li 0010, Zhenyuan Ma
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Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning [PDF]
Jisuanji kexue, 2022 Most of the information works in real world are heterogeneous information networks (HIN).Network representation methods aiming to represent node data in low dimensional space have been widely used to analyze heterogeneous information networks,so as to ...
JIANG Zong-li, FAN Ke, ZHANG Jin-li
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Mathematics, 2021 Network representation learning aims to learn low-dimensional, compressible, and distributed representational vectors of nodes in networks. Due to the expensive costs of obtaining label information of nodes in networks, many unsupervised network ...
Xin Xu +5 more
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AttrHIN: Network Representation Learning Method for Heterogeneous Information Network
IEEE Access, 2021 Network representation learning can map complex network to the low dimensional vector space, capture the topological properties of networks, and reduce the time complexity and space complexity of the algorithm.
Qingbiao Zhou, Chen Wang, Qi Li
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