mag_cs_coauthor — Computer science coauthor teams in ten conferences (MAG, 2020)
- Description
A bipartite network of computer science authors and the distinct teams of coauthors that published together in ten major conferences, compiled by Amburg, Veldt and Benson from the Microsoft Academic Graph (MAG). The conferences are KDD, WWW, ICML, NeurIPS, STOC, FOCS, CVPR, ICCV, VLDB and SIGMOD. Each team node is a distinct set of authors that appear together on at least one paper from these venues; a set of authors that published several papers is represented only once, and is labeled by the conference where it published most often (sets with a tie between conferences were discarded). An edge connects each team to each of its member authors. Papers with more than 25 authors were omitted, so teams have between 2 and 25 members. Author names and publication dates are not included in the source data, and authors that appear in no team are not included.
The network has the following properties:
is_team, whether the node is a team of coauthors (otherwise an author).- Author nodes:
author_id, the (1-based) author identifier in the source data. - Team nodes:
venue, the conference where the team published most often (one of the ten names above). In the source data one team (authors 8, 12 and 57248) has an invalid venue code (67279); its venue is left empty.
- Tags
- Social Collaboration Unweighted Metadata
- Citation
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- I. Amburg, N. Veldt, A. R. Benson, "Clustering in graphs and hypergraphs with categorical edge labels." Proceedings of The Web Conference 2020, 706-717 (2020) 10.1145/3366423.3380152 Sci-Hub
- A. Sinha, Z. Shen, Y. Song, H. Ma, D. Eide, B.-J. Hsu, K. Wang, "An Overview of Microsoft Academic Service (MAS) and Applications." Proceedings of the 24th International Conference on World Wide Web (Companion), 243-246 (2015) 10.1145/2740908.2742839 Sci-Hub
- Upstream URL OK
- https://www.cs.cornell.edu/~arb/data/cat-edge-MAG-10/
- Networks
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Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties