Titlebook: Network Embedding; Theories, Methods, a Cheng Yang,Chuan Shi,Maosong Sun Book 2021 Springer Nature Switzerland AG 2021

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Network Embedding for Community-Structured Graphss of the graph. Nevertheless, vertices in many complex networks also exhibit significant global patterns widely known as communities. In community-structured graphs, nodes in the same community tend to connect densely, and share common attributes. These patterns are expected to improve NE and benefi

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Network Embedding for Heterogeneous Graphs aims to embed multiple types of nodes into a low-dimensional space. Although most HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of NE. In this chapter, we take the

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Future Directions of Network Embeddinga scales and the development of deep learning techniques, there are also new challenges and opportunities for next-stage researches of network embedding. In the last chapter, we will look into the future directions of NRL. Specifically, we will consider the following directions including employing a

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n the collection. This generalization of commutativity is the subject of many classical theorems due to Engel, Kolchin, Kaplansky, McCoy and others. The concept has been extended to collections of bounded linear operators on Banach spaces: such a collection is defined to be triangularizable if there

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