- 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](https://github.com/allenai/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](https://github.com/WestHealth/pyvis) with some customizations to [VisJS](https://visjs.github.io/vis-network/docs/network/) 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.