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Network Representation Learning: A Survey [PDF]
IEEE Transactions on Big Data, 2020 Accepted by IEEE transactions on Big Data; 25 pages, 10 tables, 6 figures and 127 ...
Daokun Zhang, Jie Yin, Xingquan Zhu
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Recipe Representation Learning with Networks
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021Learning effective representations for recipes is essential in food studies for recommendation, classification, and other applications. Unlike what has been developed for learning textual or cross-modal embeddings for recipes, the structural relationship among recipes and food items are less explored.
Yijun Tian 0001 +3 more
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A survey on heterogeneous network representation learning
Pattern Recognition, 2021Abstract Heterogeneous information networks usually contain different kinds of nodes and distinguishing types of relations, which can preserve more information than homogeneous information networks. Heterogeneous network representation learning attempts to learn a low-dimensional representation for each node and capture rich semantic information of ...
Yu Xie, Bin Yu, Maoguo Gong
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Contrastive representation learning on dynamic networks
Neural Networks, 2023Representation learning for dynamic networks is designed to learn the low-dimensional embeddings of nodes that can well preserve the snapshot structure, properties and temporal evolution of dynamic networks. However, current dynamic network representation learning methods tend to focus on estimating or generating observed snapshot structures, paying ...
Pengfei Jiao +6 more
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On Representation Learning for Road Networks
ACM Transactions on Intelligent Systems and Technology, 2020Informative representation of road networks is essential to a wide variety of applications on intelligent transportation systems. In this article, we design a new learning framework, called Representation Learning for Road Networks (RLRN), which explores various intrinsic properties of road networks to learn embeddings of intersections and
Mengxiang Wang +3 more
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Learning IP network representations
ACM SIGCOMM Computer Communication Review, 2019We present DIP, a deep learning based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet.
Mingda Li +3 more
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Learning Network Representation Through Reinforcement Learning
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020Network Representation Learning embeds each node in a network into a low-dimensional real-value vector which can be used for downstream tasks such as link prediction and recommendation. Many existing approaches use unsupervised or (semi-)supervised methods to explore the network topology and learn representations from it.
Siqi Shen +6 more
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Personality2vec: Network Representation Learning for Personality
2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC), 2020Online social networks have made tremendous progress in recent years. People generate a large amount of text information in which the linguistic and the semantic information could convey a great deal of information about the users’ personality. Studies on personality analysis based on text information from online social networks have gradually ...
Zhanming Guan +3 more
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Hierarchical Representation Learning for Attributed Networks
IEEE Transactions on Knowledge and Data Engineering, 2021Network representation learning, also called network embedding, aiming to learn low dimensional vectors for nodes while preserving essential properties of the network, benefits plenty of practical applications. However, how to do representation learning on the network quickly and effectively is a meaningful and challenging task, especially for the ...
Shu Zhao 0005 +5 more
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Sequence to Sequence Network for Learning Network Representation
2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 2019Network representation learning is an important way for learning the low dimensional vector of nodes in the network, with preserving certain structural information between nodes in the original graph. Most existing network embedding models use truncated random walks and shallow architectures which do not fully obtain the nonlinear information and ...
Qi Liang 0002 +5 more
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