Demo Data from EDBT 2026 / reviewers in the wild / expert
Philip Bogaars
dblp:429/6675
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2026
—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 2021Graphics, 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.
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 61% Approximation and online algorithms · 30% Mathematical optimization · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Approximation and online algorithms › approximation algorithms
network design |
1.0 | 1 | 2026 | Network Restoration Games with Quotas (Student Abstract) · AAAI 2026 |
Algorithmic game theory and mechanism design
network games |
1.0 | 1 | 2026 | Network Restoration Games with Quotas (Student Abstract) · AAAI 2026 |
Algorithmic game theory and mechanism design › matching › stability concept
swap stability |
1.0 | 1 | 2026 | Network Restoration Games with Quotas (Student Abstract) · AAAI 2026 |
Mathematical optimization
combinatorial optimization |
0.3 | 1 | 2026 | Network Restoration Games with Quotas (Student Abstract) · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
polynomial-time algorithm · 1.0game theory · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Network Restoration Games with Quotas (Student Abstract)abstractIn a game of Network Restoration Games With Quotas, there is an underlying graph where a subset of its edges have to be restored by a set of agents. Each agent has a creation cost for each such edge, a traversal cost for every edge of the graph, and in addition they have a quota on the number of edges they have to restore. Then, given a set of edges that fulfill the quota, the cost of an agent is the cost of creating these edges, plus the cost of reaching them, i.e., the traversal cost. We prove that any cost-minimizing allocation is swap-stable, i.e., there is no profitable exchange of edges between any pair of agents, but computing one is hard even on trees. We complement this by designing an algorithm that finds a swap-stable allocation on trees in polynomial time and we quantify its cost against the optimal one. Philip Bogaars, Argyrios Deligkas, Eduard Eiben, Michail Fasoulakis |
AAAI | 1 |