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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 Comparison with Interpretable Contrastive Network Representation Learning. [PDF]
J Data Sci Stat Vis, 2022 Identifying unique characteristics in a network through comparison with another network is an essential network analysis task. For example, with networks of protein interactions obtained from normal and cancer tissues, we can discover unique types of interactions in cancer tissues.
Fujiwara T, Zhao J, Chen F, Yu Y, Ma KL.
europepmc +4 more sources
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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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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Network Representation Learning
大数据, 2015 Along with the constant growth of massive online social networks such as Facebook,Twitter,Weixin and Weibo,a tremendous amount of network data sets are generated.How to represent the data is an important aspect when we apply machine learning techniques ...
Weizheng Chen, Yan Zhang, Xiaoming Li
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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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Efficient Network Representation Learning via Cluster Similarity
Data Science and Engineering, 2023 Network representation learning is a de facto tool for graph analytics. The mainstream of the previous approaches is to factorize the proximity matrix between nodes.
Yasuhiro Fujiwara +5 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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Heterogeneous Network Representation Learning [PDF]
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020 Representation learning has offered a revolutionary learning paradigm for various AI domains. In this survey, we examine and review the problem of representation learning with the focus on heterogeneous networks, which consists of different types of vertices and relations.
Yuxiao Dong +4 more
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