Demo Data from SIGIR 2026 / reviewers in the wild / expert
Andrew Y. Wu
dblp:74/3278
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author
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.
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% | |
| Theoretical computer science
1 paper |
Graph algorithms and graph theory · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining › clustering
graph clustering |
0.0 | 1 | 2004 | Mining scale-free networks using geodesic clustering · KDD 2004 |
Graph algorithms and graph theory › network analysis › complex networks
scale-free networks |
0.0 | 1 | 2004 | Mining scale-free networks using geodesic clustering · KDD 2004 |
Methods — techniques the papers use, named apart from their topics
multilevel mesh · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Mining scale-free networks using geodesic clusteringabstractMany real-world graphs have been shown to be scale-free--- vertex degrees follow power law distributions, vertices tend to cluster, and the average length of all shortest paths is small. We present a new model for understanding scale-free networks based on multilevel geodesic approximation, using a new data structure called a multilevel mesh. Using this Andrew Y. Wu, Michael Garland, Jiawei Han 0001 |
KDD | 1 |