Evidence map›Paper›PMID 27930705›Full record

ArticlePloS one2016

On the Wiener Polarity Index of Lattice Networks.

Lin Chen, Tao Li, Jinfeng Liu, Yongtang Shi, Hua Wang

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lin ChenCenter for Combinatorics and LPMC-TJKLC, Nankai University, Tianjin, China.
Tao LiCollege of Computer and Control Engineering, Nankai University, Tianjin 300071, China.
Jinfeng LiuCenter for Combinatorics and LPMC-TJKLC, Nankai University, Tianjin, China.
Yongtang ShiCenter for Combinatorics and LPMC-TJKLC, Nankai University, Tianjin, China.
Hua WangDepartment of Mathematical Sciences Georgia Southern University, Statesboro, GA 30460-8093, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Network structures are everywhere, including but not limited to applications in biological, physical and social sciences, information technology, and optimization. Network robustness is of crucial importance in all such applications. Research on this topic relies on finding a suitable measure and use this measure to quantify network robustness. A number of distance-based graph invariants, also known as topological indices, have recently been incorporated as descriptors of complex networks. Among them the Wiener type indices are the most well known and commonly used such descriptors. As one of the fundamental variants of the original Wiener index, the Wiener polarity index has been introduced for a long time and known to be related to the cluster coefficient of networks. In this paper, we consider the value of the Wiener polarity index of lattice networks, a common network structure known for its simplicity and symmetric structure. We first present a simple general formula for computing the Wiener polarity index of any graph. Using this formula, together with the symmetric and recursive topology of lattice networks, we provide explicit formulas of the Wiener polarity index of the square lattices, the hexagonal lattices, the triangular lattices, and the 33 ⋅ 42 lattices. We also comment on potential future research topics.

Indexed as

Models, TheoreticalMathematics

Identifiers

PMID27930705
PMCPMC5145185

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.