On the Forgotten Eccentricity Connectivity Topological Index of Pseudo-regular Graphs

For a graph G=(V,E), the second degree of a vertex v in a graph G is the number of its second neighbors, that is the number of vertices in G having distance 2 to v. In this manuscript we have computed the Forgotten eccentricity connectivity topological index G is defined as, F\xi^{c}(G)= \sum_{v\in V(G)}d^{3}(v).e(v) and F\xi^{c}(G)= \sum_{v\in V(G)}d_{2}^{3}(v).e(v), where d^{3}(v) and d_{2}^{3}(v) are the degree of the vertices u and v and e(v) is a eccentricity of the vertices u and v. In this paper the Forgotten eccentricity connectivity topological index of pseudo-regular graphs are determined.

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2025-02-21
DOI
https://doi.org/10.36948/ijfmr.2025.v07i01.36357
Primary Topic
Graph Labeling and Dimension Problems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

On the Forgotten Eccentricity Connectivity Topological Index of Pseudo-regular Graphs

Shiladhar Pawar
International Journal For Multidisciplinary Research
Graph Labeling and Dimension Problems
article

On the Forgotten Eccentricity Connectivity Topological Index of Pseudo-regular Graphs

Shiladhar Pawar
article en

Abstract

For a graph G=(V,E), the second degree of a vertex v in a graph G is the number of its second neighbors, that is the number of vertices in G having distance 2 to v. In this manuscript we have computed the Forgotten eccentricity connectivity topological index G is defined as, F\xi^{c}(G)= \sum_{v\in V(G)}d^{3}(v).e(v) and F\xi^{c}(G)= \sum_{v\in V(G)}d_{2}^{3}(v).e(v), where d^{3}(v) and d_{2}^{3}(v) are the degree of the vertices u and v and e(v) is a eccentricity of the vertices u and v. In this paper the Forgotten eccentricity connectivity topological index of pseudo-regular graphs are determined.

International Journal For Multidisciplinary ResearchVol. 7(1)
Openalex Percentile: Top 96%
Graph Labeling and Dimension Problems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.