Demo Data from EDBT 2026 / reviewers in the wild / expert
Pallav Agrawal
dblp:318/9944
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
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 |
Graph learning · 67% Learning paradigms · 33% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Graph learning › efficient graph learning › data-efficient graph learning
active learning on graphs |
0.6 | 1 | 2022 | ALLIE: Active Learning on Large-scale Imbalanced Graphs · WWW 2022 |
Machine learning › Learning paradigms
class imbalance |
0.6 | 1 | 2022 | ALLIE: Active Learning on Large-scale Imbalanced Graphs · WWW 2022 |
Machine learning › Graph learning
graph neural network |
0.6 | 1 | 2022 | ALLIE: Active Learning on Large-scale Imbalanced Graphs · WWW 2022 |
Data mining
anomaly detection |
0.2 | 1 | 2022 | ALLIE: Active Learning on Large-scale Imbalanced Graphs · WWW 2022 |
Data mining › anomaly detection
fraud detection |
0.2 | 1 | 2022 | ALLIE: Active Learning on Large-scale Imbalanced Graphs · WWW 2022 |
Methods — techniques the papers use, named apart from their topics
graph neural network · 1.1active learning · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | ALLIE: Active Learning on Large-scale Imbalanced GraphsabstractHuman labeling is time-consuming and costly. This problem is further exacerbated in extremely imbalanced class label scenarios, such as detecting fraudsters in online websites. Active learning selects the most relevant example for human labelers to improve the model performance at a lower cost. However, existing methods for active learning for graph data often assumes that both data and label distributions are balanced. These assumptions fail in extreme rare-class classification scenarios, such as classifying abusive reviews in an e-commerce website. Limeng Cui, Xianfeng Tang, Sumeet Katariya, Nikhil Rao 0001, Pallav Agrawal, Karthik Subbian, Dongwon Lee 0001 |
WWW | 5 |