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相关概念视频

Time-Series Graph00:54

Time-Series Graph

4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.3K
Multiple Bar Graph01:07

Multiple Bar Graph

5.0K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.0K
Bar Graph01:07

Bar Graph

15.9K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
15.9K
Run Charts01:12

Run Charts

41
Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
41
Interpreting Run Charts01:25

Interpreting Run Charts

51
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
51

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相关实验视频

Updated: May 23, 2025

Brain Mapping Using a Graphene Electrode Array
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Brain Mapping Using a Graphene Electrode Array

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使用交易图形数据集进行比特币研究.

Hugo Schnoering1,2, Michalis Vazirgiannis3,4

  • 1LIX, Ecole Polytechnique, Palaiseau, 91120, France. hschnoering@coinshares.com.

Scientific data
|March 8, 2025
PubMed
概括

本研究介绍了有史以来最大的比特币交易图表数据集,其中包括时间注释和标记子集,用于高级区块链分析. 该数据集使得使用图形神经网络进行欺诈检测和网络分析的新研究成为可能.

科学领域:

  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学
  • 区块链技术 区块链技术

背景情况:

  • 比特币的分散性需要强大的分析工具.
  • 现有的数据集缺乏全面的区块链研究的规模和时间细节.
  • 图形分析的进步可以揭示加密货币交易中的复杂模式.

研究的目的:

  • 为了呈现比特币交易的最大的公开可用的,暂时注释的图形数据集.
  • 促进区块链分析,网络分析和监督学习方面的研究.
  • 为实体识别和节点分类任务提供标记子集.

主要方法:

  • 构建一个大规模的图形数据集,有25200万个节点和7.85亿个边缘.
  • 所有节点和边缘的时间标记用于时间分析.
  • 标记子集的创建:34,000个实体类型的节点和100,000个名称/类型的地址.

主要成果:

  • 该数据集是比特币交易网络分析的最广泛的资源.
  • 基于使用图形神经网络进行节点分类而建立的基线性能.
  • 在欺诈检测,网络分析和时间图学习方面已证明适用性.

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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结论:

  • 发布的数据集显著推进了区块链分析领域.
  • 它支持广泛的应用,从加密货币取证到时间网络研究.
  • 对数据集,代码和基准的开放访问促进了进一步的研究和开发.