忠诚DE:通过忠诚节点发现和强调,提高图形神经网络的性能
Haotong Wei1, Yinlin Zhu1, Xunkai Li1
1Shandong University, School of Mechanical, Electrical and Information Engineering, Weihai, 264209, China.
概括
图形神经网络 (GNN) 可以通过解决图形监督忠诚问题来改进. LoyalDE 识别并强调高质量的标记节点,增强半监督学习中的 GNN 性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 图形神经网络 (GNN) 越来越多地用于半监督学习,实现高精度.
- 然而,标记节点的监督信息的质量往往被忽视.
- 监管质量不平等可能导致GNN性能低于最佳,这个问题被称为图表监管忠诚度.
研究的目的:
- 引入图表监督忠诚度的概念,作为改善GNN的新视角.
- 提出一种量化节点忠诚度的方法以及利用这些信息的培训策略.
- 为了证明图表监督忠诚度对现有GNN模型的影响.
主要方法:
- 开发了FT-Score以根据本地特征和拓相似性量化节点忠诚度.
- 提出了LoyalDE,这是一种模型不可知训练策略,可以发现并强调忠诚节点.
- LoyalDE将培训套件扩展到高忠诚度节点,并在培训期间强调它们.
主要成果:
- 图表监督忠诚度问题显著降低了大多数现有GNN的性能.
- 在瓦尼拉GNN上,LoyalDE实现了高达9.1%的性能改善.
- 在半监督节点分类方面,LoyalDE的表现始终优于最先进的培训策略.
结论:
- 图表监督忠诚度是影响GNN性能的一个关键因素.
- 通过利用节点忠诚度,LoyalDE提供了一种有效的方法来缓解这个问题.
- 拟议的方法增强了GNN用于半监督节点分类任务.
相关概念视频
Reducing Line Loss
180
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
180
Neural Circuits
1.3K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.3K
Ogive Graph
5.7K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
5.7K
Protein Networks
4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K
End Point Prediction: Gran Plot
396
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
396
Long-term Potentiation
55.4K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
55.4K


