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Multi-Task Network Representation Learning [PDF]
Frontiers in Neuroscience, 2020 Networks, such as social networks, biochemical networks, and protein-protein interaction networks are ubiquitous in the real world. Network representation learning aims to embed nodes in a network as low-dimensional, dense, real-valued vectors, and ...
Yu Xie +4 more
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Network Representation Learning With Community Awareness and Its Applications in Brain Networks [PDF]
Frontiers in Physiology, 2022 Previously network representation learning methods mainly focus on exploring the microscopic structure, i.e., the pairwise relationship or similarity between nodes.
Min Shi, Bo Qu, Xiang Li, Cong Li
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Network Representation Based on the Joint Learning of Three Feature Views
Big Data Mining and Analytics, 2019 Network representation learning plays an important role in the field of network data mining. By embedding network structures and other features into the representation vector space of low dimensions, network representation learning algorithms can provide
Zhonglin Ye +4 more
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Social Network Forensics Analysis Model Based on Network Representation Learning [PDF]
EntropyThe rapid evolution of computer technology and social networks has led to massive data generation through interpersonal communications, necessitating improved methods for information mining and relational analysis in areas such as criminal activity. This
Kuo Zhao +6 more
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Evaluation for Instructional Interaction Using Bipartite Network Representation Learning [PDF]
Jisuanji kexue yu tansuo, 2023 With the combination and development of “Internet plus Education”, online education has become an important teaching mode at present. Research shows that the interaction in online education provides effective help for learners.
WANG Xuecen, ZHANG Yu, ZHAO Changkuan, CHEN Mo, YU Ge
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Role-Based Network Representation Learning Method [PDF]
Jisuanji gongcheng, 2021 Network representation learning is widely used to obtain the characteristics and semantics of network nodes. The existing network representation learning methods mainly study the adjacency matrix or the power of the adjacency matrix,making a node in the ...
XU You, WANG Xiaoping, XIONG Yun
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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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Attributed Bipartite Network Representation Learning
Jisuanji kexue yu tansuo, 2021 Existing network embedding models are mostly designed for homogeneous networks or heterogeneous networks, but ignore the special features of bipartite network which arise in recommender systems, search engines, question answering systems and so on ...
ZHAO Xueli, LU Guangyue, LV Shaoqing, ZHANG Pan
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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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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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