Explore connected papers in a visual graph
One of the important things when collecting previous studies is that these are related to each other, in order to make it easier for the researcher to compare them and extract what interests him, including the similarities and differences between them. .
All you have to do is enter the DOI number of the scientific article you want to search, and it will show you directly pulling its counterparts from other articles.
How does it work?
- To create each graph, we analyze an order of ~50,000 papers and select the few dozen with the strongest connections to the origin paper.
- In the graph, papers are arranged according to their similarity. That means that even papers that do not directly cite each other can be strongly connected and very closely positioned. Connected Papers is not a citation tree.
- Our similarity metric is based on the concepts of Co-citation and Bibliographic Coupling. According to this measure, two papers that have highly overlapping citations and references are presumed to have a higher chance of treating a related subject matter.
- Our algorithm then builds a Force Directed Graph to distribute the papers in a way that visually clusters similar papers together and pushes less similar papers away from each other. Upon node selection we highlight the shortest path from each node to the origin paper in similarity space.
- Our database is connected to the Semantic Scholar Paper Corpus (licensed under ODC-BY). Their team has done an amazing job of compiling hundreds of millions of published papers across many scientific fields.
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