Refine
Document Type
- Doctoral thesis (1)
Has Fulltext
- yes (1)
Is part of the Bibliography
- no (1)
Year of publication
- 2023 (1)
Language
- English (1)
Keywords
- Weighted graph (1) (remove)
Institute
Vertex and edge orderings of graphs are commonly used in algorithmic graph theory. Such orderings can encode structural properties of graphs in a condensed way and, thus, they can be used to process a graph efficiently. A common approach to find particular vertex orderings are graph searches. Here, we study the complexity of deciding whether vertex orderings with special properties can be found by particular graph searches. The properties of these orderings concern their end-vertices, their search trees or constraints encoded by partial orders.
Many graph classes can be characterized via special vertex orderings. We introduce another example, the semi-proper interval graphs, generalizations of connected proper interval graphs that are characterized via special variants of perfect elimination orderings. We study the structure of these graphs, present a linear-time recognition algorithm and show that they share some strong properties on Hamiltonian paths and cycles with proper interval graphs.
We also generalize the notion of graph classes to the case of edge-weighted graphs. A weighted graph is a member of such a weighted class if subgraphs containing edges of particular weights are members of the respective unweighted graph class. We present conditions on an unweighted graph class that ensure a linear-time recognition algorithm for its corresponding weighted graph class. This conditions make use of a novel edge monotonicity and of particular edge orderings. We apply this result to three well-known graph classes, namely split graphs, threshold graphs, and chain graphs.
Finally, we consider dynamic algorithms on threshold and chain graphs. These algorithms update certain properties of a graph after small modifications are applied to its vertex or edge set. We present certifying dynamic recognition algorithms of these classes, i.e., if the graph leaves the class after the modification, we can prove this by providing a forbidden induced subgraph of bounded size. Building on these results, we extend the notion of dynamic recognition algorithms to whole sets of edges and vertices. Furthermore, we present dynamic algorithms for the Hamiltonian path and cycle problems.