Demo Data from EDBT 2026 / reviewers in the wild / expert
Carlo Abate
dblp:282/7958
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 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 · 100% | |
| Theoretical computer science
1 paper |
Graph algorithms and graph theory · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Graph learning
graph neural network |
0.9 | 1 | 2025 | MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks · ICLR 2025 |
Machine learning › Graph learning › graph neural network
graph pooling |
0.9 | 1 | 2025 | MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks · ICLR 2025 |
Graph algorithms and graph theory › graph cut
max-cut |
0.9 | 1 | 2025 | MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks · ICLR 2025 |
Machine learning › Graph learning › graph neural network › heterophily
heterophilic graph |
0.3 | 1 | 2025 | MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks · ICLR 2025 |
Methods — techniques the papers use, named apart from their topics
hierarchical pooling · 1.7differentiable optimization · 1.7MAXCUT · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networksabstractWe propose a novel approach to compute the MAXCUT in attributed graphs, i.e., graphs with features associated with nodes and edges. Our approach works well on any kind of graph topology and can find solutions that jointly optimize the MAXCUT along with other objectives. Based on the obtained MAXCUT partition, we implement a hierarchical graph pooling layer for Graph Neural Networks, which is sparse, trainable end-to-end, and particularly suitable for downstream tasks on heterophilic graphs. Carlo Abate, Filippo Maria Bianchi |
ICLR | 1 |