Results 11 to 20 of about 1,274,940 (254)
Road Network Topology-aware Trajectory Representation Learning [PDF]
Jisuanji kexue, 2023 The approaches developed for task trajectory representation learning(TRL) on road networks can be divided into the following two categories,i.e.,recurrent neural network(RNN) and long short-term memory (LSTM) based sequence models,and the self-attention ...
CHEN Jiajun, CHEN Wei, ZHAO Lei
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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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Editorial: Graph representation learning in biological network
Frontiers in Bioinformatics, 2023 Swarup Roy +2 more
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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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Multi-modal Network Representation Learning [PDF]
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020 In today's information and computational society, complex systems are often modeled as multi-modal networks associated with heterogeneous structural relation, unstructured attribute/content, temporal context, or their combinations. The abundant information in multi-modal network requires both a domain understanding and large exploratory search space ...
Chuxu Zhang +4 more
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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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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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Scattering Networks for Hybrid Representation Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 arXiv admin note: substantial text overlap with arXiv:1703 ...
Zagoruyko +8 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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