cora_linqs — Cora machine learning papers (LINQS, 2000)

Description

A directed citation network of machine learning papers, from the version of the Cora dataset distributed by the LINQS group (Sen et al.) as a benchmark for collective classification. Cora was a search engine for computer science research papers, built by McCallum et al. by automatically crawling the web for papers and extracting their references, which were classified into a hierarchy of topics. This dataset is a selection of the papers in the machine learning topic, each assigned to one of seven subtopics, chosen such that every paper cites or is cited by at least one other paper of the selection. Each paper is described by the presence or absence of 1,433 words of a vocabulary obtained after stemming and removing stopwords, and discarding words that appear in fewer than 10 papers. The words themselves are not included in the data. This is a different version of the Cora data from the one in the cora entry.

An edge from paper A to paper B means that A cites B. The network has the following properties:

id: the identifier of the paper in the source data.
subject: the machine learning subtopic of the paper, one of Case_Based, Genetic_Algorithms, Neural_Networks, Probabilistic_Methods, Reinforcement_Learning, Rule_Learning and Theory.
words: a vector of 1,433 binary values, where the i-th value is 1 if the i-th word of the vocabulary appears in the paper, and 0 otherwise.

Tags

Informational Citation Unweighted Metadata Text

Citation

A. K. McCallum, K. Nigam, J. Rennie, K. Seymore, "Automating the Construction of Internet Portals with Machine Learning." Information Retrieval 3(2), 127-163 (2000) 10.1023/A:1009953814988 Sci-Hub
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Gallagher, T. Eliassi-Rad, "Collective Classification in Network Data." AI Magazine 29(3), 93-106 (2008) 10.1609/aimag.v29i3.2157 Sci-Hub

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https://linqs.org/datasets/#cora

Networks

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

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
cora_linqs	2,708	5,429	2.00	5.31	9.03	224.05	-0.07	0.10	19	0.92	Directed	Unipartite	id subject words		94 KiB	150 KiB	149 KiB	37 KiB