scholarly journals A Note on “Wiener Index of a Fuzzy Graph and Application to Illegal Immigration Networks”

2021 ◽  
Vol 12 (1) ◽  
pp. 304
Author(s):  
Hoon Lee ◽  
Xue-gang Chen ◽  
Moo Young Sohn
Keyword(s):  
Molecular Biology ◽  
Fuzzy Sets ◽  
Facility Location ◽  
Communication Networks ◽  
Polynomial Time ◽  
Illegal Immigration ◽  
Wiener Index ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Fuzzy Graph

Connectivity parameters have an important role in the study of communication networks. Wiener index is such a parameter with several applications in networking, facility location, cryptology, chemistry, and molecular biology, etc. In this paper, we show two notes related to the Wiener index of a fuzzy graph. First, we argue that Theorem 3.10 in the paper “Wiener index of a fuzzy graph and application to illegal immigration networks, Fuzzy Sets and Syst. 384 (2020) 132–147” is not correct. We give a correct statement of Theorem 3.10. Second, by using a new operator on matrix, we propose a simple and polynomial-time algorithm to compute the Wiener index of a fuzzy graph.

Algorithmic aspects of k-part degree restricted domination in graphs

2020 ◽  
Vol 12 (05) ◽  
pp. 2050057
Author(s):  
S. S. Kamath ◽  
A. Senthil Thilak ◽  
M. Rashmi
Keyword(s):  
Communication Networks ◽  
Polynomial Time ◽  
Dominating Set ◽  
Domination Number ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Bipartite Graphs ◽  
Chordal Graphs ◽  
Circle Graphs ◽  
Minimum Cardinality

The concept of network is predominantly used in several applications of computer communication networks. It is also a fact that the dominating set acts as a virtual backbone in a communication network. These networks are vulnerable to breakdown due to various causes, including traffic congestion. In such an environment, it is necessary to regulate the traffic so that these vulnerabilities could be reasonably controlled. Motivated by this, [Formula: see text]-part degree restricted domination is defined as follows. For a positive integer [Formula: see text], a dominating set [Formula: see text] of a graph [Formula: see text] is said to be a [Formula: see text]-part degree restricted dominating set ([Formula: see text]-DRD set) if for all [Formula: see text], there exists a set [Formula: see text] such that [Formula: see text] and [Formula: see text]. The minimum cardinality of a [Formula: see text]-DRD set of a graph [Formula: see text] is called the [Formula: see text]-part degree restricted domination number of [Formula: see text] and is denoted by [Formula: see text]. In this paper, we present a polynomial time reduction that proves the NP -completeness of the [Formula: see text]-part degree restricted domination problem for bipartite graphs, chordal graphs, undirected path graphs, chordal bipartite graphs, circle graphs, planar graphs and split graphs. We propose a polynomial time algorithm to compute a minimum [Formula: see text]-DRD set of a tree and minimal [Formula: see text]-DRD set of a graph.

scholarly journals A polynomial-time algorithm for the discrete facility location problem with limited distances and capacity constraints

2017 ◽  
Vol 14 (2) ◽  
pp. 136
Author(s):  
Isaac F. Fernandes ◽  
Daniel Aloise ◽  
Dario J. Aloise ◽  
Thiago P. Jeronimo
Keyword(s):  
Facility Location ◽  
Polynomial Time ◽  
Location Problem ◽  
Facility Location Problem ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Capacity Constraints ◽  
Distance Functions ◽  
Discrete Version ◽  
Discrete Facility Location

The objective in terms of the facility location problem with limited distances is to minimize the sum of distance functions from the facility to its clients, but with a limit on each of these distances, from which the corresponding function becomes constant. The problem is applicable in situations where the service provided by the facility is insensitive after given threshold distances. In this paper, we propose a polynomial-time algorithm for the discrete version of the problem with capacity constraints regarding the number of served clients. These constraints are relevant for introducing quality measures in facility location decision processes as well as for justifying the facility creation.

Learning Importance of Preferences

10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski
Keyword(s):  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Scoring Rules ◽  
Np Hard ◽  
Individual Preferences

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.

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