aminer_cross_domain — AMiner computer science subfields, papers and authors (1990-2005)

Description

Bipartite networks of papers and their authors in five subfields of computer science, extracted from AMiner (ArnetMiner) by J. Tang, S. Wu, J. Sun and H. Su to study cross-domain collaboration recommendation. The data cover papers published from 1990 to 2005 in the main venues of each subfield:

data_mining: KDD, SDM, ICDM, WSDM and PKDD.
medical_informatics: Journal of the American Medical Informatics Association, Journal of Biomedical Informatics, Artificial Intelligence in Medicine, IEEE Transactions on Medical Imaging and IEEE Transactions on Information Technology in Biomedicine.
theory: STOC, FOCS and SODA.
visualization: CVPR, ICCV, VAST, TVCG, IEEE Visualization and Information Visualization.
database: SIGMOD, VLDB and ICDE.

Nodes are papers and authors (identified by their names), and an undirected edge connects a paper to each of its authors.

The networks have the following properties:

is_paper: whether the node is a paper (otherwise it is an author).
name: the title of the paper or the name of the author.
venue (papers): the publication venue (empty for authors).
year (papers): the year of publication (-1 for authors).

The abstracts of the papers (replaced by the titles when missing) are not included.

Tags

Social Collaboration Affiliation Unweighted Metadata

Citation

J. Tang, S. Wu, J. Sun, H. Su, "Cross-domain collaboration recommendation." Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1285-1293 (2012) 10.1145/2339530.2339730 Sci-Hub

Upstream URL OK

https://web.archive.org/web/20141102002646/http://arnetminer.org/collaboration
https://lfs.aminer.cn/lab-datasets/crossdomain/

Networks

Tip: click on a column header to sort the list, or hover over it 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

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$\oslash$	$S$	Kind	Mode	NPs	gt	GraphML	GML	csv
data_mining	5,620	6,176	2.20	2.12	5.32	1240.27	-0.04	32	0.49	Undirected	Bipartite	is_paper name venue year	87 KiB	113 KiB	111 KiB	112 KiB
medical_informatics	10,386	11,072	2.13	2.08	5.75	2617.26	-0.08	40	0.34	Undirected	Bipartite	is_paper name venue year	164 KiB	209 KiB	204 KiB	217 KiB
theory	6,708	9,464	2.82	3.59	5.78	153.89	-0.23	22	0.87	Undirected	Bipartite	is_paper name venue year	114 KiB	150 KiB	147 KiB	153 KiB
visualization	9,602	10,990	2.29	2.34	5.02	609.24	-0.09	35	0.64	Undirected	Bipartite	is_paper name venue year	144 KiB	190 KiB	186 KiB	196 KiB
database	10,671	14,706	2.76	3.84	7.17	204.05	-0.13	24	0.80	Undirected	Bipartite	is_paper name venue year	162 KiB	221 KiB	215 KiB	224 KiB