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.
• The nodes with the highest centrality will be the most important nodes (papers) in our semantic network. Centrality Wiki
• There are several types of centrality depending on the problem statement Link.
• Since our goal is to find the most important papers that connect all the other papers in our corpus, we'll use Betweenness Centrality by default. Feel free to test out other centrality algorithms using the Select Centrality Measure dropdown.
• The plot is generated using Plotly 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.