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Network Representation Learning with Attributes and Heterogeneity. [PDF]
, 2019 Network Representation Learning (NRL) aims at learning a low-dimensional latent representation of nodes in a graph while preserving the graph information. The learned representation enables to easily and efficiently perform various machine learning tasks.
Nasrullah, Sheikh
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Graph Representation Learning on Street Networks
ISPRS International Journal of Geo-Information, 2023 Street networks provide an invaluable source of information about the different temporal and spatial patterns emerging in our cities. These streets are often represented as graphs where intersections are modeled as nodes and streets as edges between them.
Mateo Neira, Roberto Murcio
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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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Robust and fast representation learning for heterogeneous information networks
Frontiers in Physics, 2023 Network representation learning is an important tool that can be used to optimize the speed and performance of downstream analysis tasks by extracting latent features of heterogeneous networks. However, in the face of new challenges of increasing network
Yong Lei +5 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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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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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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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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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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