语义和关系意识的神经网络模型用于双类多关系异质图的双类多关系异质图
Yufei Zhao1, Hua Liu1, Hua Duan1
1College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, Shandong, China.
iScience
|April 15, 2025
概括
研究人员开发了一种新的神经网络模型,即语义和关系意识的双类多关系异质图形网络 (SRA-BMHN),用于分析复杂的图形数据. 这种模型有效地整合了语义和关系信息,以生成高级节点嵌入.
科学领域:
- 图形神经网络的神经网络
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 异质图包含多种节点和边缘类型,这给表示学习带来了挑战.
- 现有的模型往往难以有效地捕捉这些复杂的图形中的语义细微差别和关系结构.
研究的目的:
- 提出一种新的神经网络模型,SRA-BMHN,专门为双类多关系异质图设计.
- 通过有效地整合语义和关系信息来增强节点嵌入生成.
主要方法:
- 从现实数据中构建了三个双类多关系异质图.
- 开发了一个使用非线性映射和关注关系语义的语义意识模块.
- 实现了一个关系意识模块,用于拓和关系特征提取的层次二分位子图聚合.
主要成果:
- 拟议的SRA-BMHN模型在生成节点嵌入方面表现出卓越的性能.
- 在三个数据集上的实验结果验证了语义和关系意识方法的有效性.
- 该模型成功地融合了多样化的语义和关系信息,以改进表示.
结论:
- SRA-BMHN提供了一种强大的新方法,用于对双类多关系异质图的表示学习.
- 语义和关系意识的整合对于捕捉复杂的图形结构至关重要.
- 拟议的方法显示了各种下游应用,涉及异质图形数据的显著潜力.
相关概念视频
Structural Classification of Joints
3.0K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.0K
Multi-input and Multi-variable systems
88
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
88
Stereotype Content Model
13.9K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
13.9K
Functional Classification of Joints
3.7K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
3.7K
Storage
50
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
50
Aggregates Classification
290
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
290


