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fpga-dcop-36     (Miscellaneous Networks)

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This network is in the collection of Miscellaneous Networks





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Metadata

Tags
AuthorR. Hoekstra
Date2003
Edge weightsWeighted
Metadatasubsequent circuit simulation problem
DescriptionSandia/fpga_dcop_36 circuit simulation matrix. Sandia National Lab.

Please cite the following if you use the data:

@inproceedings{nr,
     title={The Network Data Repository with Interactive Graph Analytics and Visualization},
     author={Ryan A. Rossi and Nesreen K. Ahmed},
     booktitle={AAAI},
     url={https://networkrepository.com},
     year={2015}
}

Note that if you transform/preprocess the data, please consider sharing the data by uploading it along with the details on the transformation and reference to any published materials using it.

Network Statistics

Nodes1.2K
Edges5.9K
Density-
Maximum degree72
Minimum degree1
Average degree4.82950819672
Assortativity-
Number of triangles-
Average number of triangles-
Maximum number of triangles-
Average clustering coefficient-
Fraction of closed triangles-
Maximum k-core-
Lower bound of Maximum Clique-

Data Preview

Interactive visualization of fpga_dcop_36's graph structure

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Interactive Visualization of Node-level Properties and Statistics

Tools for Interactive Exploration of Node-level Statistics

Visualize and interactively explore fpga-dcop-36 and its important node-level statistics!

  • Each point represents a node (vertex) in the graph.
  • A subset of interesting nodes may be selected and their properties may be visualized across all node-level statistics. To select a subset of nodes, hold down the left mouse button while dragging the mouse in any direction until the nodes of interest are highlighted.This feature allows users to explore and analyze various subsets of nodes and their important interesting statistics and properties to gain insights into the graph data
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  • We also have tools for interactively visualizing, comparing, and exploring the graph-level properties and statistics.
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Interactive Visualization of Node-level Feature Distributions

Node-level Feature Distributions

coloring distribution

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coloring CDF

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coloring CCDF

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degree distribution

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degree CDF

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degree CCDF

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kcore distribution

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kcore CDF

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kcore CCDF

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triangle distribution

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triangle CDF

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triangle CCDF

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All visualizations and analytics are interactive and flexible for exploratory analysis and data mining in real-time and include the following features:

  • Degree, k-core, triangles, and triangle-core distributions. We include plots for each of the fundamental graph features and counts of the number with a particular property (i.e., number of nodes that form k triangles or have degree k, etc.)
  • We also include the CDF and CCDF distributions for each graph in the collection.
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