… graph regularization loss [26], or task-dependent prediction … Compared to our main graph
learning baseline LDS, our … matrix and accuracy converge quickly. This empirically verifies the …

… be solved as a link prediction task, and protein interface prediction can be regarded as a …
to help researchers acquire essential knowledge of graph representation learning and its wide …

… in graph representation learning. In particular, our model is fundamentally based on the Graph
… Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv …

… There exist two main models for dynamic graphs. Discrete-… -specific prediction eg node
classification or edge prediction. … tasks and datasets while being faster than previous methods. …

… our graph datasets by comparing their basic graph statistics … Through extensive benchmark
experiments, we highlight that … large-scale graphs and make accurate prediction under the …

… essential operations of existing machine learning algorithms … First, it acts as a quick
reference to graph learning for … prediction results need to be handled by graph learning [21]. …

… and improving machine learning predictions. You'll walk … increasingly sophisticated, it’s
essential to make use of the rich … In this chapter, we’ll quickly cover different methods for graph …

… the prediction task and the heterogeneous graph structure, … ML models, we believe that
discovering essential … Overall, we believe it is promising to explore how to quickly generate a …

… rank faster than removing normal edges as demonstrated in … To learn effective representation
of graph data, two main … of labels so that fθ can predict labels of unlabeled nodes. The …

… (19,20) Among its many applications, the development of fast, surrogate ML models for
property prediction has arguably received the most interest for its potential in accelerating …