Demo Data from VLDB 2026 / reviewers in the wild / expert
Daniel Lopatta
dblp:294/4714
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 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.
| Theoretical computer science
1 paper |
Graph algorithms and graph theory · 56% Distributed computing theory · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Graph algorithms and graph theory › graph algorithms
effective resistance estimation |
0.5 | 1 | 2021 | Local Algorithms for Estimating Effective Resistance · KDD 2021 |
Distributed computing theory
local algorithms |
0.5 | 1 | 2021 | Local Algorithms for Estimating Effective Resistance · KDD 2021 |
Graph algorithms and graph theory
graph clustering |
0.1 | 1 | 2021 | Local Algorithms for Estimating Effective Resistance · KDD 2021 |
Methods — techniques the papers use, named apart from their topics
local algorithms · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Local Algorithms for Estimating Effective ResistanceabstractEffective resistance is an important metric that measures the similarity of two vertices in a graph. It has found applications in graph clustering, recommendation systems and network reliability, among others. In spite of the importance of the effective resistances, we still lack efficient algorithms to exactly compute or approximate them on massive graphs. Pan Peng 0001, Daniel Lopatta, Yuichi Yoshida, Gramoz Goranci |
KDD | 2 |