
LPWC is a heterogenous graph machine learning dataset based on the RDF knowledge graph Linked Papers With Code (version from 2023-06-24). LPWC contains four node types - papers (376,557 nodes), datasets (8,322 nodes), tasks (4,267 nodes) and methods (2,101), and six edge types. Each node has rich semantic node features for node representation (content-based and topology-based node features are available). More information can be found in README.txt and on https://github.com/davidlamprecht/AutoRDF2GML
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
