Fair Graph Learning 大多数图学习算法都依赖于深度神经网络,所产生的向量可能已经包含了不想要的敏感信息。网络中存在的偏置被强化了,因此,将公平指标整合到图学习算法中以解决固有的偏置问题具有重要意义。 Interpretability of Graph Learning 图学习的模型一般都很复杂,因为它同时包含了图结构和特征信息。图学习算法的...
” International Conference on Learning Representations, 2017. [130] B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” Proceed- ings of the Twenty-Seventh International...
5.5 Poisson learning 6.1 Generalization: Perspective of encoder-decoder 6.2 Shallow embedding and deep embedding 7. SHALLOW GRAPH EMBEDDING 7.1 Factorization-based methods 7.2 Random-walk-based methods 7.3 Relationshipbetweenrandom-walk-basedand factorization-based methods 7.4 Limitations of shallow embedding ...
Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD)in particular. However, there are still unsolved problems to guarantee reliability and safety of automated systems, especially to effectively incorporate all available information and ...
Our methods apply to the scope of graph-based deep learning methods for DTA prediction Full size image DTA prediction, distinct from DTI prediction, aims to accurately predict the precise binding affinity between a drug and a target. This challenge, commonly seen as a regression task, has ...
For this purpose, many graph learning methods have been developed to search potential drug candidates with fast speed and low cost. In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized their architectures and hyperparameters, making them lose advantage...
In this section, these methods are collectively referred to as graph representation learning methods and are further divided into methods based on two-stage training and methods based on end-to-end training. 3.3.1 Two-stage training-based Unsupervised graph representation learning aims to learn low...
ZhenyuYangMQ/Awesome-Graph-Level-Learning Star55 Awesome graph-level learning methods. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in graph representation learning, graph regression and graph classificat...
Graphlets and path-based methods Graphlets:记录所有子图结构在整个图中出现了多少次。graphlet kernel枚举特定大小的所有可能的图形结构,并计算它们在整个图形中出现的次数。Figure 2.2 illustrates the various graphlets of size 3。 这种方法所面临的挑战是,尽管已经提出了许多近似方法,但对这些graphlets进行计数是...
Paper tables with annotated results for A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems