> For the complete documentation index, see [llms.txt](https://measures.gitbook.io/multiple-measures/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://measures.gitbook.io/multiple-measures/progress/week-5.md).

# Week 5-6

## New Idea

A heatmap for node, link level distribution over a certain local measure.

* x-axis: time (foldable according to aggregation level, suitable for O(N) measures)
* y-axis: bins for the measure
* hue: number of links / nodes

drawback: ineffective to discover the changes of active nodes.

![use a  pillar to encode distribution?](https://3721256156-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LjlQrTHR3_jKnoq29j0%2F-LlcNqiz1_BrwZX2dNth%2F-LlcO0-EHyI1JkaeecLT%2Fimage.png?alt=media\&token=1d75c5de-43e5-4497-9f8c-ca98577cc330)

![convert the box plot to a streamgraph? make the timeline brushable](https://3721256156-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LjlQrTHR3_jKnoq29j0%2F-LlcNqiz1_BrwZX2dNth%2F-LlcQsI8G5UQrXQWdcAz%2Fimage.png?alt=media\&token=a1318626-8dd3-47a6-914d-6100d52f6743)

![implementation on degree distribution over time](https://3721256156-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LjlQrTHR3_jKnoq29j0%2F-LlgplMa33898WhGGrMC%2F-LlgpnD6m_R4dTpqkpxl%2Fimage.png?alt=media\&token=4a705a2d-2f96-45c6-a70a-62d2e4ad46c5)

A radar chart for group level (global) measures (updated at each slide)

![](https://3721256156-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LjlQrTHR3_jKnoq29j0%2F-LlgpsMpeeOc5YBafIC4%2F-Llgq0Pk0P5-qde1aqvt%2Fimage.png?alt=media\&token=3100dce5-af8e-4b24-9ec6-3880dd467703)

drawback: less effective to show values -> table / parallel coordinate

## Data Loading Strategy

* Pre-computed
  * High complexity static local measures of every timestamp
    * when brushed, encode the derived value \~ max, min, ave, std
    * hypothesis: single measure on aggregated graph is of no use; we brush for a closer look at changes during the period
* Compute on demand
  * Dynamic local measures at brushed period
  * Global measures at each brushed period
    * selected groups
    * whole network

## Issue

is link-specific measures wanted? will there be difference between nodes and links?

do we need a measure table/list?

is it worthwhile to aggregate nodes into a super-node and re-analyze on measures?

## To Do

1. Group (subgraph) selection (lasso), global statics radar), subgraph node-link
2. heatmap view
3. re-arrange the web page
4. think about a clearer transition from global to subgraph then to local