Results 21 to 30 of about 1,274,940 (254)
Network Representation Learning Algorithm Based on Complete Subgraph Folding
Mathematics, 2022 Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space. Network representation learning enables the reduction of temporal and spatial complexity in the downstream ...
Dongming Chen +4 more
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3DPointCaps++: Learning 3D Representations with Capsule Networks
International Journal of Computer Vision, 2022 AbstractWe present 3DPointCaps++ for learning robust, flexible and generalizable 3D object representations without requiring heavy annotation efforts or supervision. Unlike conventional 3D generative models, our algorithm aims for building a structured latent space where certain factors of shape variations, such as object parts, can be disentangled ...
Yongheng Zhao +5 more
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A Network Representation Learning Model Based on Multiple Remodeling of Node Attributes
Mathematics, 2023 Current network representation learning models mainly use matrix factorization-based and neural network-based approaches, and most models still focus only on local neighbor features of nodes.
Wei Zhang +3 more
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DyHNet: Learning Dynamic Heterogeneous Network Representations
Information Sciences, 2022 Abstract Many real-world networks, such as social networks, contain structuralheterogeneity and experience temporal evolution. However, while therehas been growing literature on network representation learning, only afew have addressed the need to learn representations for dynamic hetero-geneous networks. The objective of our work in this paper
Hoang Nguyen +3 more
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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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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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Network Representation Learning: A Survey [PDF]
IEEE Transactions on Big Data, 2020 Accepted by IEEE transactions on Big Data; 25 pages, 10 tables, 6 figures and 127 ...
Daokun Zhang +3 more
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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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Deep Learning IP Network Representations [PDF]
Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, 2018 We present DIP, a deep learning based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet.
Mingda Li +3 more
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