PubTrends

PubTrends is a powerful tool designed to help researchers navigate the ever-growing flow of scientific publications.
It accelerates trend analysis and the discovery of breakthrough papers, offering a streamlined approach to staying ahead in your field.

With PubTrends, you can:

• Gain a concise overview of your research area.
• Explore popular trends and impactful publications.
• Discover new and promising research directions.

Datasets

• Pubmed 30 mln papers and 175 mln citations
• Semantic Scholar 170 mln papers and 600 mln citations

Workflow

Example

Here we describe the analysis for the predefined search query "human aging".

Papers

The Papers section offers a comprehensive overview of the field, featuring the total number of articles, extracted topics, and a word cloud of frequently used terms in titles and abstracts (clickable for accessing related documents). It also includes a yearly summary of published papers and an option to view papers in a list format.

Topics by Year

The Topics by Year plot displays the distribution of papers across different topics, highlighting the number of papers for each topic annually.
Each topic is summarized with key keywords and its overall share of total papers.

Top Cited Papers

The Top Cited Papers plot offers an interactive visualization of the most-cited papers, categorized by citation count and type, with different colors representing various article types.

Hot Papers

The Hot Papers plot highlights the papers with the highest number of citations for each year.

Keywords

The Keywords Frequency plot displays the most common terms used in papers and their evolution over time.
You can hover over a keyword to see the exact number of papers in which it appears.

Network

Topics are closely related groups of documents. Aggregated graph and text embeddings are used to find similar papers and detect topics.
Overall structure of topics within a research field can be visualised as a papers similarity graph.

Interactive Visualization

The displayed graph represents the overall structure of the research field, but the dedicated graph explorer offers a more comprehensive experience.
It allows papers to be color-coded by year or topic and provides advanced search and filtering options using available metadata.
In the screenshot, papers are color-coded by topic, with one paper highlighted along with its connected papers.

Topics

Topics are identified through hierarchical clustering of paper embeddings.
Users can explore the topic hierarchy using a dendrogram in a dedicated plot.

Topic Keywords

For each topic, the application displays a familiar word cloud and an articles plot.
The word cloud is generated using terms specific to the selected topic, highlighting their significance compared to other topics.
The size of each word reflects its importance, based on the proportion of papers mentioning it.

Review

Generate a review for the chosen topic - a set of sentences from top cited papers with the highest probability to be included in a real review paper.

Materials

• Shpynov, O. and Kapralov, N., PubTrends: a scientific literature explorer. In Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 2021 (pp. 1-1). https://doi.org/10.1145/3459930.3469501
• Nikiforovskaya, A., Kapralov, N., Vlasova, A., Shpynov, O. and Shpilman, A., 2020, December. Automatic generation of reviews of scientific papers. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 314-319). IEEE. https://arxiv.org/abs/2010.04147

Code

Source code is available on GitHub at https://github.com/JetBrains-Research/PubTrends.