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Related Concept Videos

Adjusting a Traverse01:12

Adjusting a Traverse

In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...
Graphs of Functions01:30

Graphs of Functions

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...
Direct Motor Pathways01:11

Direct Motor Pathways

The direct motor pathways, also known as the pyramidal tracts, are a group of neural pathways that originate in the brain and descend through the spinal cord. They control the voluntary movement of the body. There are two major direct motor pathways: the corticospinal and the corticobulbar tracts.
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Interference: Path Lengths01:10

Interference: Path Lengths

Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
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Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
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Related Experiment Video

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A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Tugging graphs faster: efficiently modifying path-preserving hierarchies for browsing paths.

Daniel Archambault1, Tamara Munzner, David Auber

  • 1INRIA Bordeaux Sud-Ouest, Universite de Bordeaux I, 351 cours de la Liberation, Bat A30, 33405 Talence, France. daniel.archambault@inria.fr

IEEE Transactions on Visualization and Computer Graphics
|April 28, 2010
PubMed
Summary
This summary is machine-generated.

TugGraph explores graph proximity by modifying hierarchies to reveal node and subgraph connections. This system efficiently visualizes large graphs, preserving structure for meaningful exploration.

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Area of Science:

  • Computer Science
  • Graph Theory
  • Data Visualization

Background:

  • Graph visualization systems often use hierarchies for organizing large graphs.
  • Existing methods detect features globally, neglecting local node proximity.
  • This global approach limits exploration of specific nodes or subgraphs within the larger structure.

Purpose of the Study:

  • To introduce TugGraph, a novel system for exploring paths and proximity around nodes and subgraphs.
  • To enable users to understand how a node or subgraph extends into the larger graph context.
  • To provide efficient and meaningful visualizations of large-scale graph data.

Main Methods:

  • TugGraph modifies pre-existing graph hierarchies to focus on local structures.
  • It guarantees path-preserving hierarchies, ensuring visualization fidelity.
  • The system processes large graphs (hundreds of thousands of nodes, millions of edges) without requiring a full graph layout.

Main Results:

  • TugGraph effectively visualizes paths and proximity around selected graph components.
  • It presents proximal information within the context of the entire graph.
  • The system delivers these visualizations in seconds, even for massive datasets.

Conclusions:

  • TugGraph offers a powerful new approach to exploring large graph structures.
  • It overcomes limitations of global feature detection by focusing on local proximity.
  • The system provides efficient, meaningful, and context-aware graph exploration capabilities.