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Include small world in plot_network_measures #131

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Include small world in plot_network_measures#131
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wingedRuslan

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@KirstieJane
KirstieJane
opened on Jul 5, 2019

At the moment plot_network_measures only includes assortativity, clustering, shortest path length, efficiency and modularity.

It really should also include the small world coefficient.

On thing that's important to note is that the small world coefficient is calculated for the real graph compared to each of the random graphs.

This means that - in comparison to the other measures - the variability will be on the "blue" bars - the bars representing the real graphs - rather than the grey ones (the random graphs).

That bar should be set to exactly 1.

The command: bundleGraphs.report_small_world("real_graph") returns a dictionary with keys real_graph_R0, real_graph_R1, real_graph_R2 etc. These correspond to the comparison of the graph labelled "real_graph" with R0 (itself), R1, R2 etc.

Another thing to note is that I don't think the small world measures are saved as part of the graph bundle after they've been calculated. It might be interesting to see if we can make it so you only have to run it once (similar to making random graphs etc).

(This might be better as its own issue, just shout if that's the case.)

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Include small world in plot_network_measures · Issue #131 · WhitakerLab/scona · GitHub
    Skip to content
    Include small world in plot_network_measures #131
    Open
    Open
    Include small world in plot_network_measures#131
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    wingedRuslan
    Description
    @KirstieJane
    KirstieJane
    opened on Jul 5, 2019
    At the moment plot_network_measures only includes assortativity, clustering, shortest path length, efficiency and modularity.
    It really should also include the small world coefficient.
    On thing that's important to note is that the small world coefficient is calculated for the real graph compared to each of the random graphs.
    This means that - in comparison to the other measures - the variability will be on the "blue" bars - the bars representing the real graphs - rather than the grey ones (the random graphs).
    That bar should be set to exactly 1.
    The command: bundleGraphs.report_small_world("real_graph") returns a dictionary with keys real_graph_R0, real_graph_R1, real_graph_R2 etc. These correspond to the comparison of the graph labelled "real_graph" with R0 (itself), R1, R2 etc.
    Another thing to note is that I don't think the small world measures are saved as part of the graph bundle after they've been calculated. It might be interesting to see if we can make it so you only have to run it once (similar to making random graphs etc).
    (This might be better as its own issue, just shout if that's the case.)
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Include small world in plot_network_measures · Issue #131 · WhitakerLab/scona · GitHub
      Skip to content
      Include small world in plot_network_measures #131
      Open
      Open
      Include small world in plot_network_measures#131
      Assignees
      wingedRuslan
      Description
      @KirstieJane
      KirstieJane
      opened on Jul 5, 2019
      At the moment plot_network_measures only includes assortativity, clustering, shortest path length, efficiency and modularity.
      It really should also include the small world coefficient.
      On thing that's important to note is that the small world coefficient is calculated for the real graph compared to each of the random graphs.
      This means that - in comparison to the other measures - the variability will be on the "blue" bars - the bars representing the real graphs - rather than the grey ones (the random graphs).
      That bar should be set to exactly 1.
      The command: bundleGraphs.report_small_world("real_graph") returns a dictionary with keys real_graph_R0, real_graph_R1, real_graph_R2 etc. These correspond to the comparison of the graph labelled "real_graph" with R0 (itself), R1, R2 etc.
      Another thing to note is that I don't think the small world measures are saved as part of the graph bundle after they've been calculated. It might be interesting to see if we can make it so you only have to run it once (similar to making random graphs etc).
      (This might be better as its own issue, just shout if that's the case.)
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Include small world in plot_network_measures · Issue #131 · WhitakerLab/scona · GitHub
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        Include small world in plot_network_measures #131
        Open
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        Include small world in plot_network_measures#131
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        wingedRuslan
        Description
        @KirstieJane
        KirstieJane
        opened on Jul 5, 2019
        At the moment plot_network_measures only includes assortativity, clustering, shortest path length, efficiency and modularity.
        It really should also include the small world coefficient.
        On thing that's important to note is that the small world coefficient is calculated for the real graph compared to each of the random graphs.
        This means that - in comparison to the other measures - the variability will be on the "blue" bars - the bars representing the real graphs - rather than the grey ones (the random graphs).
        That bar should be set to exactly 1.
        The command: bundleGraphs.report_small_world("real_graph") returns a dictionary with keys real_graph_R0, real_graph_R1, real_graph_R2 etc. These correspond to the comparison of the graph labelled "real_graph" with R0 (itself), R1, R2 etc.
        Another thing to note is that I don't think the small world measures are saved as part of the graph bundle after they've been calculated. It might be interesting to see if we can make it so you only have to run it once (similar to making random graphs etc).
        (This might be better as its own issue, just shout if that's the case.)
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Include small world in plot_network_measures · Issue #131 · WhitakerLab/scona · GitHub
          Skip to content
          Include small world in plot_network_measures #131
          Open
          Open
          Include small world in plot_network_measures#131
          Assignees
          wingedRuslan
          Description
          @KirstieJane
          KirstieJane
          opened on Jul 5, 2019
          At the moment plot_network_measures only includes assortativity, clustering, shortest path length, efficiency and modularity.
          It really should also include the small world coefficient.
          On thing that's important to note is that the small world coefficient is calculated for the real graph compared to each of the random graphs.
          This means that - in comparison to the other measures - the variability will be on the "blue" bars - the bars representing the real graphs - rather than the grey ones (the random graphs).
          That bar should be set to exactly 1.
          The command: bundleGraphs.report_small_world("real_graph") returns a dictionary with keys real_graph_R0, real_graph_R1, real_graph_R2 etc. These correspond to the comparison of the graph labelled "real_graph" with R0 (itself), R1, R2 etc.
          Another thing to note is that I don't think the small world measures are saved as part of the graph bundle after they've been calculated. It might be interesting to see if we can make it so you only have to run it once (similar to making random graphs etc).
          (This might be better as its own issue, just shout if that's the case.)
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Include small world in plot_network_measures · Issue #131 · WhitakerLab/scona · GitHub
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            Include small world in plot_network_measures #131
            Open
            Open
            Include small world in plot_network_measures#131
            Assignees
            wingedRuslan
            Description
            @KirstieJane
            KirstieJane
            opened on Jul 5, 2019
            At the moment plot_network_measures only includes assortativity, clustering, shortest path length, efficiency and modularity.
            It really should also include the small world coefficient.
            On thing that's important to note is that the small world coefficient is calculated for the real graph compared to each of the random graphs.
            This means that - in comparison to the other measures - the variability will be on the "blue" bars - the bars representing the real graphs - rather than the grey ones (the random graphs).
            That bar should be set to exactly 1.
            The command: bundleGraphs.report_small_world("real_graph") returns a dictionary with keys real_graph_R0, real_graph_R1, real_graph_R2 etc. These correspond to the comparison of the graph labelled "real_graph" with R0 (itself), R1, R2 etc.
            Another thing to note is that I don't think the small world measures are saved as part of the graph bundle after they've been calculated. It might be interesting to see if we can make it so you only have to run it once (similar to making random graphs etc).
            (This might be better as its own issue, just shout if that's the case.)
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Include small world in plot_network_measures · Issue #131 · WhitakerLab/scona · GitHub
              Skip to content
              Include small world in plot_network_measures #131
              Open
              Open
              Include small world in plot_network_measures#131
              Assignees
              wingedRuslan
              Description
              @KirstieJane
              KirstieJane
              opened on Jul 5, 2019
              At the moment plot_network_measures only includes assortativity, clustering, shortest path length, efficiency and modularity.
              It really should also include the small world coefficient.
              On thing that's important to note is that the small world coefficient is calculated for the real graph compared to each of the random graphs.
              This means that - in comparison to the other measures - the variability will be on the "blue" bars - the bars representing the real graphs - rather than the grey ones (the random graphs).
              That bar should be set to exactly 1.
              The command: bundleGraphs.report_small_world("real_graph") returns a dictionary with keys real_graph_R0, real_graph_R1, real_graph_R2 etc. These correspond to the comparison of the graph labelled "real_graph" with R0 (itself), R1, R2 etc.
              Another thing to note is that I don't think the small world measures are saved as part of the graph bundle after they've been calculated. It might be interesting to see if we can make it so you only have to run it once (similar to making random graphs etc).
              (This might be better as its own issue, just shout if that's the case.)
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Include small world in plot_network_measures · Issue #131 · WhitakerLab/scona · GitHub
                Skip to content
                Include small world in plot_network_measures #131
                Open
                Open
                Include small world in plot_network_measures#131
                Assignees
                wingedRuslan
                Description
                @KirstieJane
                KirstieJane
                opened on Jul 5, 2019
                At the moment plot_network_measures only includes assortativity, clustering, shortest path length, efficiency and modularity.
                It really should also include the small world coefficient.
                On thing that's important to note is that the small world coefficient is calculated for the real graph compared to each of the random graphs.
                This means that - in comparison to the other measures - the variability will be on the "blue" bars - the bars representing the real graphs - rather than the grey ones (the random graphs).
                That bar should be set to exactly 1.
                The command: bundleGraphs.report_small_world("real_graph") returns a dictionary with keys real_graph_R0, real_graph_R1, real_graph_R2 etc. These correspond to the comparison of the graph labelled "real_graph" with R0 (itself), R1, R2 etc.
                Another thing to note is that I don't think the small world measures are saved as part of the graph bundle after they've been calculated. It might be interesting to see if we can make it so you only have to run it once (similar to making random graphs etc).
                (This might be better as its own issue, just shout if that's the case.)
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