Results 221 to 230 of about 30,518 (252)

Unifying community detection and network embedding in attributed networks

Knowledge and Information Systems, 2021
Traditionally, community detection and network embedding are two separate tasks. Network embedding aims to output a vector representation for each node in the network, and community detection aims to find all densely connected groups of nodes and well separate them from others.
Yu Ding, Hao Wei, Guyu Hu, Zhisong Pan, Shuaihui Wang   +4 more
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Structured subspace embedding on attributed networks

Information Sciences, 2020
Abstract Attributed network embedding aims to learn low-dimensional node vector representations in the network. To date, the primary strategy of the existing approaches is to combine topological structure with attribute information based on the homophily assumption imposed on the whole attributed networks. However, this strategy ignores the formation
Zhongjing Yu, Zhong Zhang 0004, Haoran Chen, Junming Shao   +3 more
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Attributed Multi-layer Network Embedding

2018 IEEE International Conference on Big Data (Big Data), 2018
Network embedding has gained much attention in recent years. Embedding network into a low-dimensional vector space has shown promising performance in many graph mining tasks such as node classification, link prediction, and community detection. However, in many real-world applications, a variety of networks could be abstracted and presented in a ...
Zhongyue Pei, Xi Zhang 0008, Fangwei Zhang, Binxing Fang   +3 more
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Translation-Based Attributed Network Embedding

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 2018
Attributed network embedding, which aims to map the structural and attribute information into a latent vector space jointly, has attracted a surge of research attention in recent years. However, a vast majority of existing work explores the correlation between node structure and attribute values whereas the attribute type information which can be ...
Jingjie Mo, Neng Gao, Yujing Zhou, Yang Pei, Jiong Wang   +4 more
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Binarized Attributed Network Embedding via Neural Networks

2020 International Joint Conference on Neural Networks (IJCNN), 2020
Traditional attributed network embedding methods are designed to map structural and attribute information of networks jointly into a continuous Euclidean space, while recently a novel branch of them named binarized attributed network embedding has emerged to learn binary codes in Hamming space, aiming to save time and memory costs and to naturally fit ...
Hangyu Xia, Neng Gao, Jia Peng, Jingjie Mo, Jiong Wang   +4 more
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Label Informed Attributed Network Embedding

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017
Attributed network embedding aims to seek low-dimensional vector representations for nodes in a network, such that original network topological structure and node attribute proximity can be preserved in the vectors. These learned representations have been demonstrated to be helpful in many learning tasks such as network clustering and link prediction ...
Xiao Huang 0001, Jundong Li, Xia Hu 0001
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A Robust Embedding for Attributed Networks with Outliers

2019
Network embedding, as a promising tool, aims to learn low-dimensional embeddings for nodes in a network. Most existing methods work well when the topological structure is closely correlated to node attributes. However, real-world networks often contain outliers that have abnormal attributes.
Cheng Zhang, Le Zhang, Yuanye He, Daren Zha   +3 more
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Attributed Network Embedding with Micro-Meso Structure

ACM Transactions on Knowledge Discovery from Data, 2018
Recently, network embedding has received a large amount of attention in network analysis. Although some network embedding methods have been developed from different perspectives, on one hand, most of the existing methods only focus on leveraging the plain network structure, ignoring the abundant attribute information of nodes.
Juan-Hui Li, Ling Huang 0002, Chang-Dong Wang 0001, Dong Huang 0001, Jian-Huang Lai, Pei Chen 0001   +5 more
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Attributed Network Embedding via a Siamese Neural Network

2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2019
Recently, network embedding has attracted a surge of attention due to its ability to automatically extract features from graph-structured data. Though network embedding method has been intensively studied, most of the existing approaches pay attention to either structures or attributes.
Jiong Wang, Neng Gao, Jia Peng, Jingjie Mo   +3 more
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Network-Word Embedding for Dynamic Text Attributed Networks

2018 IEEE 12th International Conference on Semantic Computing (ICSC), 2018
Network embedding enables to apply off-the-shelf machine learning methods to the nodes on the network. Leveraging the textual information associated with nodes into network embedding methods is advantageous. However, only a few works try to leverage textual information to network embeddings.
Hiroyoshi Ito, Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa   +3 more
openaire   +1 more source