The scholarly literature is a vast store of formalized human knowledge, interconnected by citations between publications.

Looking at these citations is one way to measure the influence of scholarly research. Metrics like h-index—prominently displayed by services such as Google Scholar—measure influence by counting citations. But with more complete data about these citations getting better and more accessible every day, we can do better.

The scholar visualization tool shows the influence a researcher has had both within his or her own field and across other fields, illustrating a local view of a scholar's network of influence and telling the story of how this influence has developed over time. The author of interest is represented as the central node in a network, and other papers that have cited papers written by this author are shown as circular nodes surrounding the central one. The animation starts early in the researcher's career, and progresses forward in time. As new papers appear, they send out links representing citations, both to the central node and to other nodes that appear in this network.

Image explaining the network diagram. The center node represents all of the papers authored by the scholar of interest. Surrounding nodes represent papers that have cited work by the scholar of interest. Lines between the nodes show citations between papers. Papers are revealed by year in a spiral formation, so that earlier papers appear closer to the center. Showing a scholar's influence: The size of each node is scaled by the Eigenfactor score of that paper—a metric of influence that takes into account its position in the total citation network. Bigger nodes represent the most influential papers that have cited the central scholar. The color of each node shows the academic discipline of the paper. A more colorful network means that the impact of the central scholar’s work has extended out to a wider range of fields. The color of the center node represents the dominant field of the central scholar—the most common field of all the scholar’s publications.

In order to reduce the visual complexity of the graph, not all of the papers that have cited the central scholar are represented. Rather, the most influential papers (by Eigenfactor score) are selected to be visualized as nodes in the network.

Below the network diagram, line charts show key indicators over time:

Image explaining the line charts. Top chart: Number of publications by the central scholar. Middle chart: The number of times that a paper authored by the central scholar was cited in each year. Bottom chart: The sum of the Eigenfactor score for each of the central scholar’s paper in each year. A higher value means that the scholar’s output in this year had a large impact. Note that since impact can take time to accumulate, more recent years tend to have lower scores. Colors on the line charts show funding from the Pew program, marking the periods before, during, and after funding.

A note on comparing authors:

Scales are relative to each author, so the size of the nodes and the y-axes on the timelines are not consistent between authors. For this reason, direct comparison between different authors is not recommended. However, comparing the relative densities of the graphs can reveal information about the types of community represented by the citation networks.

Image comparing relative densities of two different graphs. A denser network means that the papers that cite the central author also tend to cite each other. A more sparse network indicates fewer citations between papers shown in the network. This could be a result of the central scholar having impact across a wider set of academic communities.

Frequently Asked Questions

What can this show me that other services can't?

Let's take Google Scholar for example. An author's Google Scholar profile includes information about that author's publications, measures and displays of number of citations over time, and a couple of basic citation metrics including h-index. An author's profile goes beyond this by taking into account not just the number of citations a scholar has received, but where these citations are coming from. Exploring these incoming citations through our visualization and analysis tools can give a more detailed picture of the extent and quality of the influence a scholar has had.

I'm seeing papers in my visualization from [Medicine, Sociology, Psychoceramics...]. What's going on here? These are not my papers!

If you are the author at the center of the visualization, the papers that appear around you are important papers that have cited your work, not necessarily papers that you have authored. This is how the visualization shows what sort of influence the center collection may have had—by highlighting papers published since that have found value in one or more of the center papers.

Do I really need to register an account?

Having an account where you can manage your own paper collections is the best way to create meaningful collections and visualizations. We will, of course, never spam you or share your email.

I have a question/suggestion/idea! How can I get in touch with you?

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Jason Portenoy

Jason is a PhD student in the University of Washington Information School. He has a background in neuroscience and biomedical research, and currently studies data science and data visualization. He is a member of UW's DataLab, and was recently awarded a Data Science for Social Good Fellowship from the eScience Institute at UW.

Jevin West

Jevin West is an Assistant Professor at the University of Washington Information School and a Data Science Fellow at the eScience Institute. His research lies at the cross section of network science, knowledge organization and information visualization. He co-directs the DataLab at UW and is the co-founder of the Eigenfactor project. Prior to joining the faculty at UW, he was a postdoctoral fellow in the Department of Physics at Umea University in Sweden and received his PhD in Biology from UW.