Co-authorship Maps
Below is the map showing co-authorship relationships between authors in papers relating to science communication and misinformation. To see the co-authorship map for science communication only, click here.
Affiliation:
Papers:
About the Co-authorship Maps
Co-authorship maps show the social structure of a field. A node in this graph is an author. The size of the nodes represents the centrality of each author. Since the datasets are samples, we caution against reading these nodes sizes as importance measures. A link represents a shared paper. The links are weighted so that thicker lines are scaled to the number of shared papers between authors.
We provide two co-authorship graphs — one that includes research papers from science communication and misinformation research, and another that includes papers from science communication only. Colors represent clusters of authors. They are meant to help identify which authors are closer together in the network. The connected components with ten nodes or more are shown. Again, we do not include all papers ever published around these topics. Therefore, we are only seeing a part of the field. Many papers and authors are not represented that may be in subsequent updates.
To use the visualization, click and drag the nodes to move them around. If you hover over an author name in the network, you will see the papers with the highest centrality scores in our dataset. We use a pagerank-based metric to assess influence. This is similar to how Google ranks webpages.
Data and Methods
The data for this visualization come from the Microsoft Academic Graph, a dataset that uses web crawlers to find and link academic publications. Publications were linked to this dataset using Document Object Identifiers (DOI) and fuzzy title matching. The network was clustered using Infomap. (Note that this clustering is on the author network, which is different than the citation clustering in other visualizations.) The colors of the nodes are assigned based on the top level of the hierarchical clustering. The authors with the highest network centrality scores are assigned have labels next to their nodes. Since this is just a sample, we caution readers on interpreting an "importance" of authors.Contact
Please contact us if you have any questions or comments about the tools, data or other content.