A survey of information network representation learning
Journal of Hebei University of Science and Technology, 2020 The network representation learning algorithm represents the information network as a low-dimensional dense real vector carrying the characteristic information of network nodes, and is applied to the input of downstream machine learning tasks.
Junhao LU, Yunfeng XU
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Review on heterogeneous network representation learning method
Journal of Hebei University of Science and Technology, 2021 Most of the real-life networks are heterogeneous networks that contain multiple types of nodes and edges, and heterogeneous networks integrate more information and contain richer semantic information than homogeneous networks.
Jianxia WANG +3 more
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Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
Sensors, 2017 Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous ...
Haiyang Yu +4 more
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Integrating Social Circles and Network Representation Learning for Item Recommendation [PDF]
, 2019 With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms.
Wang, Can +20 more
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Research and development of network representation learning
网络与信息安全学报, 2019 Network representation learning is a bridge between network raw data and network application tasks which aims to map nodes in the network to vectors in the low-dimensional space. These vectors can be used as input to the machine learning model for social
YIN Ying, JI Lixin, HUANG Ruiyang +1 more
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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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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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Dynamic Network Representation of Information Diffusion Based on Relationship Strength Theory and Feedback Mechanism [PDF]
Jisuanji gongcheng, 2023 Most of the existing Network Representation Learning (NRL) methods can not fit well with the real world information dissemination network, and can not effectively model the time characteristics and dynamic evolution characteristics of the information ...
PAN Le, LI Bicheng, WAN Wang, ZENG Rongshen
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Low-Bit Quantization for Attributed Network Representation Learning [PDF]
, 2019 Attributed network embedding plays an important role in transferring network data into compact vectors for effective network analysis. Existing attributed network embedding models are designed either in continuous Euclidean spaces which introduce data ...
Yang, Hong +14 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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