The Microsoft Academic Graph (opens in new tab) makes it possible to gain analytic insights about any of the entities within it: publications, authors (opens in new tab), institutions (opens in new tab), topics (opens in new tab), journals (opens in new tab), and conferences (opens in new tab). In this series, we present analytic insights about current conferences, which we hope will help you prepare for attending each event. All the insights within are derived from the Microsoft Academic Graph and visualized in Microsoft Power BI. You can generate your own insights by accessing the Microsoft Academic Graph through the Academic Knowledge API (opens in new tab) or through Azure Data Lake Store (opens in new tab) (please contact us (opens in new tab) for the latter option). If you would like to learn how we generated the insights below, please see the repository with source code (opens in new tab).
In this post, we present historical trend analysis about the conference ICML – International Conference on Machine Learning, taking place in Stockholm International Fairs, Stockholm, Sweden from July 10-15, 2018. We derive insights since 1993 to the latest available year.
Click on each image for current trends and data hosted by Microsoft Academic Graph (opens in new tab).
ICML Paper Output
The chart below shows the evolution of number of conference papers for each conference year.
In the following chart, the black bars represent average numbers of references per conference paper for each year. The data show the recent publications tend to cite more references. The green bars show the average numbers of citations received by conference papers written in a given year. Note that the citations are raw counts and not normalized by the age of publications. This is because the “correct” way to normalize the citation counts turns out to be a nontrivial problem and may well be application dependent. Please treat the raw data presented as an invitation to conduct research on this topic!
That being said, a visible trend is older publications tend to receive more citations because they have more time to receive due recognitions. There are, however, notable exceptions, the first in years of 1997 and 1998 due to several highly cited papers:
- Lin, Dekang. “An Information-Theoretic Definition of Similarity.” ICML ’98 Proceedings of the Fifteenth International Conference on Machine Learning, 1998, pp. 296–304. (opens in new tab)
- Yang, Yiming, and Jan O. Pedersen. “A Comparative Study on Feature Selection in Text Categorization.” ICML ’97 Proceedings of the Fourteenth International Conference on Machine Learning, 1997, pp. 412–420. (opens in new tab)
The second, in 2001, the citation spike is mostly contributed by Lafferty, John D., et al. “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.” (opens in new tab)
Memory of References
How old are papers cited by ICML papers? Follow a given year’s column to see the age of papers cited in conference papers published that year. For example, in 2017, ICML papers collectively cited 1,774 papers published in 2016, 1,444 papers published in 2015, and so on.
*If some years appear to cite publications from the future, it is most likely because they cited books. When a new edition of the book appeared, it replaced the previous one in the Microsoft Academic Graph, and the citation appears to be from the future. In this representation, we remove all instances of papers citing papers more than two years in the future to generate a cleaner view.
Outgoing references
What venues do ICML papers cite?
The bar chart shows the top 10 venues cited by ICML papers. NIPS, ICML and JMLR emerge as the top 3.
The 100 percent stacked bar chart below shows the percent of references given by ICML papers to each of the top 10 venues, year by year.
Incoming citations
What venues cite ICML papers?
The bar chart below shows the top 10 venues of that cite ICML papers. This time, ICML is at the top, followed by NIPS and arXiv Learning. See the table for year-by-year details of citations coming from each of the top 10 venues.
The 100 percent stacked bar chart below shows the citation distribution from the top 10 citing venues, year by year.
The fact that both incoming and outgoing citations of ICML papers do not concentrate on a few venues indicates the work presented at ICML is very diverse and multidisciplinary in nature.
Most-cited authors
Who are the most-cited authors of all time in ICML papers? The chart below ranks the most-cited authors by using number of publication cited by the conference and number of citations received from the conference. Authors do not have to have published in ICML to appear on this chart.
Who are the rising stars among the top cited authors in ICML? The 100% stacked bar chart below shows the citation distribution by the top 20 authors, year by year.
Top institutions
The bubble chart visualizes the top institutions at ICML by citation count. The size of the bubble is proportional to the total number of publications from that institution at ICML.
Get the most current data and also explore the top institutions at the conference in more detail by clicking the chart. Once on the underlying Microsoft Power BI report, click on a column to rank the top institutions by publication or citation count.
Top authors
The next three charts show author rankings according to different criteria.
The bubble chart displays ICML authors ranked by citation count, with bubble size being relative to publication count.
Get the most current data and also explore the top authors at the conference in more detail by clicking the chart. Once on the underlying Microsoft Power BI report, you can also explore the top conference authors in more detail. Click on a column to rank the top authors by Microsoft Academic rank, publication, or citation count.
The bubble chart below visualizes author rank, which is calculated by Microsoft Academic by using a formula that is less susceptible to citation counts than similar measures. The X axis shows author rank. The higher an author’s rank, the closer they are to the right side. The Y axis normalizes the rank by publication count and enables us to identify impactful authors who might not have had a very large number of publications. The closer an author is to the top, the higher their normalized rank. Of course, the area of the chart that represents the highest rank is the top right corner.
We hope you have enjoyed the analytic insights into this conference made possible by the Microsoft Academic Graph (opens in new tab)! Please visit our Microsoft Academic Graph (opens in new tab) page to learn how you can use our knowledge graph to generate your own custom analytics about an institution, a topic, an author, a publication venue, or any combination of these.
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