Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

94
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...
94
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

90
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
90
Associative Learning01:27

Associative Learning

285
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
285
Attribution Theory00:56

Attribution Theory

12.9K
Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
12.9K
Structural Classification of Joints01:20

Structural Classification of Joints

3.1K
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...
3.1K
Inductive Reasoning00:59

Inductive Reasoning

59.9K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
59.9K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Broad spectrum structure discovery in large-scale higher-order networks.

Nature communications·2026
Same author

Cohesive urban bicycle infrastructure design through optimal transport routing in multilayer networks.

Journal of the Royal Society, Interface·2025
Same author

Similarity and economy of scale in urban transportation networks and optimal transport-based infrastructures.

Nature communications·2024
Same author

Structure and inference in hypergraphs with node attributes.

Nature communications·2024
Same author

No good deed goes unpunished: the social costs of prosocial behaviour.

Evolutionary human sciences·2023
Same author

Community detection in large hypergraphs.

Science advances·2023

相关实验视频

Updated: May 31, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K

在异质和归因多层网络中灵活推断.

Martina Contisciani1, Marius Hobbhahn2, Eleanor A Power3,4

  • 1Max Planck Institute for Intelligent Systems, Tübingen 72076, Germany.

PNAS nexus
|January 24, 2025
PubMed
概括

我们开发了一种灵活的概率模型,用于分析具有多种数据的复杂多层网络. 这种方法有效地处理异质信息,用于社区检测和预测任务.

关键词:
拉普拉斯的近似方法分配多层网络的多层网络.自动区分的自动区分.重叠的社区重叠的社区.概率生成模型的概率生成模型.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

456
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

相关实验视频

Last Updated: May 31, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

456
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

科学领域:

  • 网络科学 网络科学
  • 机器学习 机器学习
  • 数据挖掘 数据挖掘

背景情况:

  • 分析各种各样的节点和边缘信息复杂的网络数据集是具有挑战性的.
  • 现有的方法往往需要对异质数据进行特定模型分析.

研究的目的:

  • 开发一个统一的概率生成模型,用于任意数据类型的多层网络中的推理.
  • 创建一个可扩展和灵活的模型,适应各种输入数据组合.

主要方法:

  • 使用贝叶斯框架与拉普拉斯匹配进行参数解释.
  • 采用了用于算法实现的自动区分,消除了手动衍生.
  • 为异质多层网络开发了一个概率生成模型.

主要成果:

  • 在异质多层数据中检测重叠的社区结构的有效性.
  • 在复杂网络数据上的各种预测任务中展示了成功的表现.
  • 验证了模型在现实世界的社会支持网络中发现模式的能力.

结论:

  • 拟议的模型为分析异质多层网络提供了一个可扩展,灵活和可解释的解决方案.
  • 该方法有效地整合了各种信息,以进行强大的社区检测和预测.
  • 该方法为复杂的网络结构和关系提供了有意义的见解.