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Robert J. Cimikowski

dblp:67/383 · DBLP profile ↗
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9ranked-venue papers
8as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 6 · 5 first-authorDatabases, data management, data science and information retrieval · 3 · 2 first-authorArtificial intelligence and machine learning · 2 · 2 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.

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

TopicWeightPapersLastEvidence papers
Graph algorithms and graph theory
graph algorithms
0.011995
An Analysis of Some Heuristics for the Maximum Planar Subgraph Problem · SODA 1995
Graph algorithms and graph theory › planar graphs
maximum planar subset
0.011995
An Analysis of Some Heuristics for the Maximum Planar Subgraph Problem · SODA 1995

Methods — techniques the papers use, named apart from their topics

heuristic analysis · 0.0
YearPublicationVenuePosition
2007 Approximating the fixed linear crossing number
Robert J. Cimikowski, Brendan Mumey
Discret. Appl. Math.1
2003 A simple factor-3 approximation for labeling points with circles
Minghui Jiang 0001, Jianbo Qian, Zhongping Qin, Binhai Zhu, Robert J. Cimikowski
Inf. Process. Lett.5
2002 Algorithms for the fixed linear crossing number problem
Robert J. Cimikowski
Discret. Appl. Math.1
1996 A neural-network algorithm for a graph layout problem
abstract
We present a neural-network algorithm for minimizing edge crossings in drawings of nonplanar graphs. This is an important subproblem encountered in graph layout. The algorithm finds either the minimum number of crossings or an approximation thereof and also provides a linear embedding realizing the number of crossings found. The parallel time complexity of the algorithm is O(1) for a neural network with n(2) processing elements, where n is the number of vertices of the graph. We present results from testing a sequential simulator of the algorithm on a set of nonplanar graphs and compare its performance with the heuristic of Nicholson.
Robert J. Cimikowski, Paul Shope
IEEE Trans. Neural Networks1
1995 An Analysis of Some Heuristics for the Maximum Planar Subgraph Problem
Robert J. Cimikowski
SODA1
1995 On Heuristics for Determining the Thickness of a Graph
Robert J. Cimikowski
Inf. Sci.1
1993 On Certain Hamiltonian Inner Triangulations
Robert J. Cimikowski
Discret. Appl. Math.1
1992 Properties of some Euclidean proximity graphs
Robert J. Cimikowski
Pattern Recognit. Lett.1
1990 Finding Hamiltonian Cycles in Certain Planar Graphs
Robert J. Cimikowski
Inf. Process. Lett.1