Demo Data from EDBT 2026 / reviewers in the wild / expert
Sebastian Böttger
dblp:11/4871
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1
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% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining › structured data mining › graph mining › graph learning
graph kernels |
0.1 | 1 | 2006 | VGM: visual graph mining · SIGMOD Conference 2006 |
Data mining › structured data mining
graph mining |
0.1 | 1 | 2006 | VGM: visual graph mining · SIGMOD Conference 2006 |
Data mining › predictive modeling
classification |
0.0 | 1 | 2006 | VGM: visual graph mining · SIGMOD Conference 2006 |
Methods — techniques the papers use, named apart from their topics
support vector machine · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | VGM: visual graph miningabstractAs more and more graph data become available in various application domains, graph mining is of ever increasing importance in data management.Graph kernels are a novel and successful method for data mining in graphs. Unfortunately, implementing graph kernels is not trivial, and few applied researchers have therefore used graph kernels so far. In this demonstration, we present a Java software package called Visual Graph Mining (VGM). VGM allows the user to classify graphs using graph kernels and Support Vector Machines in a graphical user interface that is easy to learn and use. It is linked to LIBSVM for Support Vector Machine computations, yet can be easily transferred to other Support Vector Machine packages. Furthermore, VGM provides basic data mining features such as Nearest Neighbor search, graph algorithms such as Dijkstra, Floyd-Warshall, and computes and visualizes product graphs and topological indices of graphs.VGM 's homepage can be found at: http://www.cip.ifi.lmu.de/~boettger/sigmod. Karsten M. Borgwardt, Sebastian Böttger, Hans-Peter Kriegel |
SIGMOD Conference | 2 |