Results 31 to 40 of about 1,274,940 (254)
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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Active Discriminative Network Representation Learning [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations.
Li Gao +5 more
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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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Mathematics, 2021 Network representation learning aims to learn low-dimensional, compressible, and distributed representational vectors of nodes in networks. Due to the expensive costs of obtaining label information of nodes in networks, many unsupervised network ...
Xin Xu +5 more
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AttrHIN: Network Representation Learning Method for Heterogeneous Information Network
IEEE Access, 2021 Network representation learning can map complex network to the low dimensional vector space, capture the topological properties of networks, and reduce the time complexity and space complexity of the algorithm.
Qingbiao Zhou, Chen Wang, Qi Li
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Node Classification Algorithm Based on Information Propagation Node Set for CTDN [PDF]
Jisuanji gongcheng, 2021 The study described in this paper addresses the problem of node classification in Continuous-Time Dynamic Network(CTDN).In this work, an information propagation node set is defined according to the features of the actual network information propagation ...
HUANG Xin, LI Yun, XIONG Jinyu
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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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Higher-order Network Representation Learning [PDF]
Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18, 2018 This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. The HONE framework is highly expressive and flexible with many interchangeable components. The experimental results demonstrate the effectiveness of learning higher-order network representations.
Ryan A. Rossi +2 more
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A Hybrid Deep Network Representation Model for Detecting Researchers’ Communities [PDF]
Journal of Artificial Intelligence and Data Mining, 2022 Recently, network representation has attracted many research works mostly concentrating on representing of nodes in a dense low-dimensional vector. There exist some network embedding methods focusing only on the node structure and some others considering
A. Torkaman +4 more
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Hypernetwork Representation Learning with the Set Constraint
Applied Sciences, 2022 There are lots of situations that cannot be described by traditional networks but can be described perfectly by the hypernetwork in the real world. Different from the traditional network, the hypernetwork structure is more complex and poses a great ...
Yu Zhu, Haixing Zhao
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