markdown/centrality.md

- Now that we've contructed our semantic similarity network, we can use Graph Theory on this network to compute the centrality of each node using [NetworkX](https://networkx.org/).
- The nodes with the highest centrality will be the most important nodes (papers) in our semantic network. [Centrality Wiki](https://en.wikipedia.org/wiki/Centrality)
- There are several types of centrality depending on the problem statement [Link](https://neo4j.com/developer/graph-data-science/centrality-graph-algorithms/).
- Since our goal is to find the most important papers that connect all the other papers in our corpus, we'll use [Betweenness Centrality](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.betweenness_centrality.html#networkx.algorithms.centrality.betweenness_centrality) by default. Feel free to test out other centrality algorithms using the `Select Centrality Measure` dropdown.
- The plot is generated using [Plotly](https://plotly.com/python/) and is fully interactive!
    - Bars are ordered in descending order, so the most important paper in your corpus is the top bar.
    - Colored by the topic the paper belongs to.
    - Hover over any bar to view the title and truncated abstract of the paper
    - Zoom in and out by clicking and dragging over the chart. Double click to zoom out.
- Click on the `Download plot as png` button on the top right hand side corner to download a .png file of your plot.