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Time-Series Graph00:54

Time-Series Graph

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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...
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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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.
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An electrical network is a system composed of interconnected elements, such as resistors, capacitors, inductors, and voltage or current sources. Unlike a circuit, an electrical network does not necessarily form a closed path. In other words, while all circuits can be considered networks due to their interconnected nature, not every network qualifies as a circuit.
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Neurons are the main type of cell in the nervous system that generate and transmit electrochemical signals. They primarily communicate with each other using neurotransmitters at specific junctions called synapses. Neurons come in many shapes that often relate to their function, but most share three main structures: an axon and dendrites that extend out from a cell body.
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Exploring the structure and function of temporal networks with dynamic graphlets.

Y Hulovatyy1, H Chen1, T Milenković1

  • 1Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications, and ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA.

Bioinformatics (Oxford, England)
|June 15, 2015
PubMed
Summary
This summary is machine-generated.

We introduce dynamic graphlets, a novel method for analyzing temporal networks by capturing relationships between time snapshots. This approach overcomes limitations of existing methods and enhances the study of complex network structures and functions, particularly in human aging research.

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Area of Science:

  • Network Science
  • Computational Biology
  • Data Analysis

Background:

  • Temporal real-world networks are increasingly available, posing challenges for efficient analysis.
  • Static network analysis methods applied to temporal data lose valuable temporal information.
  • Existing dynamic network approaches, like temporal motifs, have limitations related to null model selection.

Purpose of the Study:

  • To develop a novel approach for explicit temporal network analysis by capturing inter-snapshot relationships.
  • To introduce dynamic graphlets as a method to overcome limitations of temporal motifs.
  • To improve the characterization of temporal network structure and function.

Main Methods:

  • Developed new theory for graphlet-based analysis of temporal networks.
  • Introduced the concept of dynamic graphlets, distinct from temporal motifs.
  • Applied dynamic graphlets to temporal age-specific molecular network data.

Main Results:

  • Dynamic graphlets effectively capture inter-snapshot relationships in temporal networks.
  • Dynamic graphlets outperform static graphlets in characterizing temporal network structure and function.
  • The method overcomes limitations associated with null model selection in temporal motif analysis.

Conclusions:

  • Accounting for temporal information significantly enhances network analysis.
  • Dynamic graphlets provide a robust framework for studying temporal networks.
  • The approach offers new insights into temporal age-specific molecular networks and human aging.