Can I read Network Representation Learning: A Survey on EtoBox?

Network Representation Learning: A Survey by Zhang, Daokun; Yin, Jie; Zhu, Xingquan; Zhang, Chengqi is a scholarly article available to read on EtoBox.

What is Network Representation Learning: A Survey about?

With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the und

Author

Zhang, Daokun; Yin, Jie; Zhu, Xingquan; Zhang, Chengqi

Published

2017

Language

EN