Demo Data from EDBT 2026 / reviewers in the wild / expert
Eduardo Rinaldi
dblp:364/4212
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 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.
| Computer graphics and multimedia
1 paper |
Geometric modeling and processing · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Geometric modeling and processing
procedural modeling |
0.7 | 1 | 2023 | NodeGit: Diffing and Merging Node Graphs · ACM Trans. Graph. 2023 |
Software maintenance and evolution › software configuration management
version control |
0.2 | 1 | 2023 | NodeGit: Diffing and Merging Node Graphs · ACM Trans. Graph. 2023 |
Methods — techniques the papers use, named apart from their topics
approximate graph edit distance · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | NodeGit: Diffing and Merging Node GraphsabstractThe use of version control is pervasive in collaborative software projects. Version control systems are based on two primary operations: diffing two versions to compute the change between them and merging two versions edited concurrently. Recent works provide solutions to diff and merge graphics assets such as images, meshes and scenes. In this work, we present a practical algorithm to diff and merge procedural programs written as node graphs. To obtain more precise diffs, we version the graphs directly rather than their textual representations. Diffing graphs is equivalent to computing the graph edit distance, which is known to be computationally infeasible. Following prior work, we propose an approximate algorithm tailored to our problem domain. We validate the proposed algorithm by applying it both to manual edits and to a large set of randomized modifications of procedural shapes and materials. We compared our method with existing state-of-the-art algorithms, showing that our approach is the only one that reliably detects user edits. Eduardo Rinaldi, Davide Sforza, Fabio Pellacini |
ACM Trans. Graph. | 1 |