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书目名称Graph Representation Learning影响因子(影响力)




书目名称Graph Representation Learning影响因子(影响力)学科排名




书目名称Graph Representation Learning网络公开度




书目名称Graph Representation Learning网络公开度学科排名




书目名称Graph Representation Learning被引频次




书目名称Graph Representation Learning被引频次学科排名




书目名称Graph Representation Learning年度引用




书目名称Graph Representation Learning年度引用学科排名




书目名称Graph Representation Learning读者反馈




书目名称Graph Representation Learning读者反馈学科排名

granite

https://doi.org/10.1007/978-3-322-84766-9In Chapter 3 we discussed approaches for learning low-dimensional embeddings of nodes. We focused on so-called . approaches, where we learn a unique embedding for each node. In this chapter, we will continue our focus on shallow embedding methods, and we will introduce techniques to deal with multi-relational graphs.

ornithology

Mikroökonomik im Bachelor-StudiumThe previous parts of this book introduced a wide variety of methods for learning representations of graphs. In this final part of the book, we will discuss a distinct but closely related task: the problem of

Gastric

Flavouring

Traditional Graph Generation ApproachesThe previous parts of this book introduced a wide variety of methods for learning representations of graphs. In this final part of the book, we will discuss a distinct but closely related task: the problem of

JUST

https://doi.org/10.1007/978-3-322-85960-0their graph position and the structure of their local graph neighborhood. In other words, we want to project nodes into a latent space, where geometric relations in this latent space correspond to relationships (e.g., edges) in the original graph or network [Hoff et al., 2002] (Figure 3.1).

JUST

歌剧等

https://doi.org/10.1007/978-3-658-41287-6on of objects (i.e., nodes), along with a set of interactions (i.e., edges) between pairs of these objects. For example, to encode a social network as a graph we might use nodes to represent individuals and use edges to represent that two individuals are friends (Figure 1.1). In the biological domai

Cumbersome

Sedative

https://doi.org/10.1007/978-3-322-83428-7nt works arising in this area, and I expect a proper overview of graph representation learning will never be truly complete for many years to come. My hope is that these chapters provide a sufficient foundation and overview for those who are interested in becoming practitioners of these techniques o