CsAuthors.net database
Most of the data is coming from the
DBLP Computer Science Bibliography
and the rest is coming from CsAuthors.net own database.
We are working hard to keep everything up-to-date. However, we know that there are many papers not yet included in our dataset.
If something is wrong or missing, feel free to write me at .
We are working hard to keep everything up-to-date. However, we know that there are many papers not yet included in our dataset.
If something is wrong or missing, feel free to write me at .
The "Dijkstra number"
The Dijkstra number describes the collaborative distance between an author and
Edsger W. Dijkstra.
In our dataset 90.0% of authors are connected to Edsger W. Dijkstra and the average Dijkstra number among them is 5.08.
These kind of number/metrics are quite famous and already well defined in other fields.
In our dataset 90.0% of authors are connected to Edsger W. Dijkstra and the average Dijkstra number among them is 5.08.
These kind of number/metrics are quite famous and already well defined in other fields.
- The "Erdős number" expresses the collaborative distance with Paul Erdős, the famous Hungarian mathematician.
- The "Bacon number" expresses the co-acting distance with Kevin Bacon.
The "Erdős number"
The Erdős number describes the collaborative distance between an author and
Paul Erdős.
In our dataset 90.0% of authors are connected to Paul Erdős and the average Erdős number among them is 4.68.
Find more on Wikipedia with an article on the"Erdős number".
In our dataset 90.0% of authors are connected to Paul Erdős and the average Erdős number among them is 4.68.
Find more on Wikipedia with an article on the"Erdős number".
Lu-Chen Weng
Orcid: 0000-0003-1475-4930
According to our database1,
Lu-Chen Weng
authored at least 3 papers
between 2022 and 2025.
Collaborative distances:
Collaborative distances:
Timeline
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Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2025
A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.
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Nat. Comput. Sci., February, 2025
Unsupervised deep learning of electrocardiograms enables scalable human disease profiling.
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npj Digit. Medicine, 2025
2022
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npj Digit. Medicine, 2022