Demo Data from EDBT 2026 / reviewers in the wild / expert
Bijan K. Varjavand
dblp:239/4980
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 50% Graph learning · 50% | |
| Databases, data mining, and information retrieval
1 paper |
Graph data management · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models › relational model
random graph model |
0.4 | 1 | 2019 | GraSPy: Graph Statistics in Python · J. Mach. Learn. Res. 2019 |
Graph data management
graph analytics |
0.1 | 1 | 2019 | GraSPy: Graph Statistics in Python · J. Mach. Learn. Res. 2019 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | GraSPy: Graph Statistics in PythonabstractWe introduce graspy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible and easy-to-use algorithms for analyzing and understanding graphs with a sklearn compliant API. graspy can be downloaded from Python Package Index (PyPi), and is released under the Apache 2.0 open-source license. The documentation and all releases are available at https://neurodata.io/graspy. Jaewon Chung, Benjamin D. Pedigo, Eric Bridgeford, Bijan K. Varjavand, Hayden S. Helm, Joshua T. Vogelstein |
J. Mach. Learn. Res. | 4 |