1.简介 这篇论文是由 acm computing survey 2024 年发布的最新图机器学习综述,涵盖图核,子图挖掘,RNN,GNN,pooling等多项重要技术,题为《State of the Art and Potentialities of Graph-level Learning》。该论文系统回顾并总结了图层级学习(Graph-level Learning)领域的发
Graph-related applications, including classification, regression, and clustering, have seen significant advancements with the development of graph neural networks (GNNs). However, a gap remains in effectively using these models for heterogeneous graphs, as current methods primarily focus on homogeneous ...
It ensures that the overall pattern and subtle node differences of the graph can be fully reflected when performing graph-level representations, thus optimizing the performance of various tasks such as graph classification and graph regression. Fig. 1 Overall architecture of graph-level representation ...
In this section, we conduct several empirical studies on both graph classification and regression tasks. All these studies were conducted on a Linux system with an Intel Core i7-8700 CPU, an Nvidia TITAN RTX, and 32 GB RAM. Conclusion and future work Considering the drawbacks of the existing...
of which has different values of a regularization control parameter, and use the outputs of the trained regression models as target-level representations. No... HI Suk,D Shen - Springer International Publishing 被引量: 7发表: 2016年 BCDForest: a boosting cascade deep forest model towards the ...
Subsequently, for node classification, we employ these representations to train and test an L2-regularized logistic regression (LR) classifier. For node clustering, we evaluate the proposed method under the clustering evaluation protocol and cluster the learned representations using the K-Means algorithm...
For the simulation of risk level prediction, we compared our method with some machine learning algorithms, such as ridge regression, Lasso regression, support vector regression, decision trees, and multi-layer perceptron. Results showed that the two-level indicator system is superior to the general ...
Traditional machine learning models such as support vector regression (SVR) and random forest (RF) have been extensively employed for mobile traffic prediction. While these models are characterized by their lightweight nature and ease of training, their ability to capture complex patterns in mobile tr...
Self-contained tests: for white-box testing, the generated tests do start/stop the application, binding to an ephemeral port. This means that the generated tests can be used forregression testing(e.g., added to the Git repository of the application, and run with any build tool such as Mav...
To support reductions in traffic crashes, there is a growing emphasis on the development of models aimed at predicting the location, severity, and causes of crashes. The early approaches relied mainly on linear regression models (Mountain et al., 1996,Greibe, 2003), models that incorporate rando...