Tight Bounds for Domination-Based Identification Problems in Block Graphs

Core Concepts

The paper establishes tight lower and upper bounds for the identifying code number, locating-dominating code number, and open locating-dominating code number of block graphs, in terms of the number of blocks and the order of the graph.

Abstract

The paper studies three domination-based identification problems in block graphs:

Identifying codes (ID-codes): The authors prove that the ID-number of a closed-twin-free block graph is bounded above by the number of blocks in the graph, verifying a conjecture from the literature.

Locating-dominating codes (LD-codes): The authors provide a general upper bound for the LD-number in terms of the number of blocks and the number of articulation vertices. They also prove that the LD-number of a twin-free block graph without isolated vertices is at most half the order of the graph, confirming a conjecture.

Open locating-dominating codes (OLD-codes): The authors establish tight lower and upper bounds for the OLD-number of block graphs, both in terms of the number of blocks and the order of the graph.

The authors provide examples of families of block graphs that attain these bounds, showing the tightness of the results. The paper complements previous work on the computational complexity of these problems and provides a comprehensive understanding of the structure of block graphs with respect to domination-based identification.

Stats

None.

Quotes

None.

Key Insights Distilled From

On three domination-based identification problems in block graphs

by Dipayan Chak... at arxiv.org 04-01-2024

https://arxiv.org/pdf/1811.09537.pdf

Deeper Inquiries

Can the bounds established in this paper be extended to other classes of graphs beyond block graphs

The bounds established in the paper for identifying, locating-dominating, and open locating-dominating codes in block graphs may not directly extend to other classes of graphs. The structural properties and characteristics of block graphs, such as the presence of blocks and articulation vertices, play a crucial role in deriving these bounds. Other classes of graphs may have different structural properties that require distinct approaches to determine similar bounds. However, the methodologies and insights gained from studying block graphs could potentially inspire new approaches for analyzing identification problems in other graph classes.

What are the implications of these results on the design and optimization of detection systems in practical applications

The results presented in the paper have significant implications for the design and optimization of detection systems in various practical applications. By understanding the minimum-sized identifying, locating-dominating, and open locating-dominating codes in block graphs, researchers and practitioners can enhance fault-diagnosis systems, improve biological testing methodologies, and optimize machine learning algorithms. These results provide insights into the efficient placement of detection devices in networks to locate intruders or faults effectively, leading to enhanced system reliability and performance.

Are there other structural properties of block graphs that could be leveraged to further refine the bounds or develop more efficient algorithms for these identification problems

There are several other structural properties of block graphs that could be leveraged to refine the bounds or develop more efficient algorithms for identification problems. For example, the concept of layers in block graphs, as discussed in the paper, could be further explored to derive tighter bounds based on the distribution of vertices across layers. Additionally, the relationship between articulation vertices and the formation of identifying codes could be studied in more detail to optimize the selection of vertices in the codes. Leveraging the unique characteristics of block graphs, such as the presence of leaf blocks and non-leaf blocks, could also lead to the development of specialized algorithms for improving the efficiency of identifying, locating-dominating, and open locating-dominating codes in these graphs.