Agsta-Net
Adaptive graph spatiotemporal attention network for citation count prediction
Bibliographic Data
| ID | 21442911 |
|---|---|
| Authors | Bin Wang (0000-0002-8591-8528, Hebei University of Technology), Feng Wu (0000-0003-0144-4423, Institute of Scientific and Technical Information), Lukui Shi (0000-0002-7906-6349, Hebei University of Technology, corresponding author) |
| Year | 2023 |
| Volume | 128 |
| Issue | 1 |
| Pages | 511-541 |
| Publication date | 2023-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Scientometrics (JOURNAL) |
| Journal identifiers | ISSN: 0138-9130 • E-ISSN: 1588-2861 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11192-022-04541-0 |
| OpenAlex | W4304147713 |
| Language | EN |
| Citations received | 2 |
| References cited | 39 |
Citation · Citation analysis · Data mining · Data science · Graph · Information retrieval · World Wide Web · Advanced Graph Neural Networks · Advanced Text Analysis Techniques · Computer Science · Theoretical Computer Science · Topic Modeling
Sleeping Beauties in science
Which factors help authors produce the highest impact research? Collaboration, journal and document properties
ArnetMiner
Characteristics of highly cited papers
DeepWalk
The History and Meaning of the Journal Impact Factor
Node2vec
Defining and identifying Sleeping Beauties in science
Long Short-Term Memory
An index to quantify an individual's scientific research output
| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |