Downloads provided by UsageCounts
This dataset contains similarity scores among articles in AMiner's DBLP v10 dataset. Similarities are calculated using the JoinSim [1] similarity measure on the derived citation network using the following metapaths: Paper - Author - Paper (PAP_similarities.csv) Paper - Topic - Paper (PTP_similarities.csv) The file ids.csv contains a mapping from AMiner's ids to our internal numeric ids used in the similarities files. [1] Xiong, Y., Zhu, Y., Yu, P.S.: Top-k similarity join in heterogeneous information networks. IEEE Transactions on Knowledge and Data Engineering 27(6), 1710– 1723 (2015)
We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).
DBLP, paper similarity, bibliometrics, scholarly knowledge graphs
DBLP, paper similarity, bibliometrics, scholarly knowledge graphs
| citations 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). | 1 | |
| 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 |
| views | 17 | |
| downloads | 19 |

Views provided by UsageCounts
Downloads provided by UsageCounts