markdown/stripnet.md

• STriP Network plots the network of the semantically similar papers and how they are connected to each other.
• A network is a graph with nodes and edges. Each node here is one paper and the edges show the similarity between papers.
• Similarity is calculated by the cosine similarity between the SPECTER embeddings of the two papers. The cosine similarity is a measure of the angle between two vectors.
• The Cosine Similarity Threshold parameter controls how similar two papers need to be, to get connected by an edge. Hover over the tooltip to get more information about it.
• The default value of the Cosine Similarity Threshold is heuristically calculated by STriPNet to give decent results. Feel free to play around with it until you are satisfied.
• The plot is generated using PyVis with some customizations to VisJS and is fully interactive!
  • Hover on a node to see the paper's title and truncated abstract.
  • Click on a node to see the edges that connect that node.
  • Zoom in and out by scrolling.
  • Click on any empty space and drag the plot to move and recenter it.
  • Click on nodes and move them around to view them better.
  • Click on the legend boxes and move them around. I like to place the legend boxes over the cluster of nodes of the same color!
  • The number on the node is the row number of the input csv file on which this paper was located. Remember that python row numbers start from 0.
• Once you are happy with how your STriP Network plot looks, right click and save image to your local.