Forbidding 发表于 2025-3-21 16:12:05
书目名称Graph Representation Learning影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0387933<br><br> <br><br>书目名称Graph Representation Learning影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0387933<br><br> <br><br>书目名称Graph Representation Learning网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0387933<br><br> <br><br>书目名称Graph Representation Learning网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0387933<br><br> <br><br>书目名称Graph Representation Learning被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0387933<br><br> <br><br>书目名称Graph Representation Learning被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0387933<br><br> <br><br>书目名称Graph Representation Learning年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0387933<br><br> <br><br>书目名称Graph Representation Learning年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0387933<br><br> <br><br>书目名称Graph Representation Learning读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0387933<br><br> <br><br>书目名称Graph Representation Learning读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0387933<br><br> <br><br>granite 发表于 2025-3-21 21:07:02
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 发表于 2025-3-22 04:14:35
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 ofGastric 发表于 2025-3-22 05:48:13
http://reply.papertrans.cn/39/3880/387933/387933_4.pngFlavouring 发表于 2025-3-22 09:20:01
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 ofJUST 发表于 2025-3-22 13:55:08
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 (Figure 3.1).JUST 发表于 2025-3-22 18:06:22
http://reply.papertrans.cn/39/3880/387933/387933_7.png歌剧等 发表于 2025-3-23 00:22:10
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 domaiCumbersome 发表于 2025-3-23 02:41:49
http://reply.papertrans.cn/39/3880/387933/387933_9.pngSedative 发表于 2025-3-23 05:49:45
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