We’re excited to announce that starting today Microsoft Academic users have a new way of visualizing academic impact. This feature, available on author, conference, journal, institution and topic detail pages, provides a visualization of the impact an entity has in relation to other types of entities. For example, this allows you to see which journals or conferences an author has most impacted with their publications, or which institutions have published the most impactful work in a specific journal.
Before we jump in, it’s important to define Impact and how it’s measured.
When our graph is built, each entity is evaluated along with its connected entities and an individual rank is assigned. This is done based on a few factors, including the rank of its connected entities through references or citations. In this way, rank is not solely driven by citation count, but also by the rank of an entity’s connections in the graph. Additionally, it is known that citations are a lagging indicator of impact because it takes time for research to be duly recognized and its impacts to be fully appreciated, leading to an age bias favoring older work. To adjust for this bias, we have employed a reinforcement learning algorithm (opens in new tab) that utilizes the massive historical data we have to train a ranker that recognizes the momentum of new publications and projects their future impacts. This way, newer work is not at a disadvantage when comparing to older work. We refer to this rank as an entity’s ‘Saliency’ (for a more detailed description of Saliency, see recent our paper (opens in new tab)). The “top” entity relationships we previously showed on entity pages already reflected impact through saliency.
It is common for an author or an institution to achieve higher impact by being prolific. Saliency ranks are therefore often conflating productivity and the impact of individual publications. While aggregate rank of impact is useful, it is also interesting to take into consideration productivity and ask; “What volume of work was done to achieve a given rank?”, and “what is the average per-article impact?”. To show this, we show the publication normalized saliency by using a feature in MAG called paper families to properly count the number of articles that should be regarded as a single publication. Paper families are a grouping of papers that we have found to be identical, or nearly identical, which have been published in different venues. Take for instance a paper an author has written that has been published in a pre-print repository, a conference and a journal. We record each of these publications as separate entity’s in the graph, but these publications represent the same work and are thus grouped into a paper family in MAG. Using this value for an entity’s publication count, we normalize the saliency and determine an entity’s productivity.
Author impact chart for the University of Washington
The chart above shows the most impactful authors at the University of Washington (opens in new tab). As you can see, Christopher J. Murry (opens in new tab) has the highest saliency rank. However, the author with the second highest saliency rank, Mohsen Naghavi (opens in new tab) has a higher productivity (publication normalized rank), 2th overall. Mohammad H. Forouzanfar (opens in new tab), with only a few publications relative to peers, has really made an impact with the work they have published, ranked 1st among the top 20 at this institution.
By default, the charts display overall rankings. If you would like to dig further, we also provide contextual year and topic filters which allow you to drill down and build a custom view. You may also be curious what publications these authors have written to achieve these rankings. If you would like to see them, simply click on the authors name and you will be taken to a search results page to view them.
Presenting real-time analytics has been a goal of our team this year. This feature is another great demonstration of how the Microsoft Academic Graph (MAG) (opens in new tab) paired with the Microsoft Academic Knowledge Exploration Service (MAKES) can be used to gather bibliometric data and tell the story behind research in real-time.
If you have any questions or comments on this feature or other features on our website, please reach out by using the feedback tab at the bottom right of our site (opens in new tab).