Results 11 to 20 of about 273,685 (258)
Hybrid Strategy Improved Sparrow Search Algorithm in the Field of Intrusion Detection
IEEE Access, 2023 Aiming at the problem that Sparrow Search Algorithm(SSA) may fall into local optima and have slow convergence speed, a hybrid strategy improved sparrow search algorithm(HSISSA) is proposed in this paper, and it is applied to feature selection and model ...
Liu Tao, Meng Xueqiang
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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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Universal network representation for heterogeneous information networks [PDF]
2017 International Joint Conference on Neural Networks (IJCNN), 2017 Network representation aims to represent the nodes in a network as continuous and compact vectors, and has attracted much attention in recent years due to its ability to capture complex structure relationships inside networks. However, existing network representation methods are commonly designed for homogeneous information networks where all the nodes
Ruiqi Hu +5 more
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MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding
Applied Sciences, 2022 Depending on the type of information network, information network embedding is classified into homogeneous information network embedding and heterogeneous information network (HIN) embedding. Compared with the homogeneous network, HIN composition is more
Tingting Liu, Jian Yin, Qingfeng Qin
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Heterogeneous Information Network Embedding with Incomplete Multi-view Fusion [PDF]
Jisuanji kexue, 2021 Heterogeneous information network (HIN) embedding maps complex heterogeneous information to a low-dimensional dense vector space,which is conducive to the calculation and storage of network data.Most existing multi-view-based HIN embedding methods ...
ZHENG Su-su, GUAN Dong-hai, YUAN Wei-wei
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Heterogeneous Information Network Representation Learning Incorporating Community Structure
IEEE Access, 2022 Network embedding usually learns the node representations using their local context information. However, as an important mesoscopic description of network structures, community structures hidden in the networks have been largely ignored.
Wei Yu +5 more
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Method of Attributed Heterogeneous Network Embedding with Multiple Features [PDF]
Jisuanji kexue, 2022 Network embedding aims to represent nodes in unstructured network with low-dimensional,real-valued vectors,so that node embedding can retain the structural and attribute features of the original network as much as possible.However,current research mainly
TANG Qi-you, ZHANG Feng-li, WANG Rui-jin, WANG Xue-ting, ZHOU Zhi-yuan, HAN Ying-jun
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Heterogeneous Information Network Embedding for Mention Recommendation
IEEE Access, 2020 Mention recommendation is the task of recommending the right candidate users in a message. Many works have been conducted on the problem of whom to mention.
Feng Yi, Bo Jiang, Jianjun Wu
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Information diffusion in interconnected heterogeneous networks [PDF]
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017 In this paper, we are interested in modeling the diffusion of information in a multilayer network using thermodynamic diffusion approach. State of each agent is viewed as a topic mixture represented by a distribution over multiple topics. We have observed and learned diffusion-related thermodynamical patterns in the training data set, and we have used ...
Shahin Mahdizadehaghdam +3 more
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Asymmetric Recommendation Algorithm in Heterogeneous Information Network
Jisuanji kexue yu tansuo, 2020 In recent years, as a new research direction, heterogeneous information networks have attracted a lot of attention in recommendation system. Most similarity measures based on heterogeneous information networks follow that the relationship between users ...
ZHAO Chuan, ZHANG Kaihan, LIANG Jiye
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