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consist of n crossed quadrilaterals and possess complex three-dimensional structures with high symmetry and dense spatial arrangements. As a result, these compounds hold great potential for applications in materials science, catalytic chemistry, and other related fields. The Kirchhoff index of a graph G is defined as the sum of resistive distances between any two vertices in G. This article focuses on studying a type of tetrafunctional compound with a linear crossed square chain shape. The Kirchhoff index and degree Kirchhoff index of this compound are calculated, and a detailed analysis and discussion is conducted. The calculation formula for the Kirchhoff index is obtained based on the relationship between the Kirchhoff index and Laplace eigenvalue, and the number of spanning trees is derived for linear crossed quadrangular chains. The obtained formula is validated using Ohm\u2019s law and Cayley\u2019s theorem. Asymptotically, the ratio of Kirchhoff index to Wiener index approaches one-fourth. Additionally, the expression for the degree Kirchhoff index of the linear crossed quadrangular chain is obtained through the relationship between the degree Kirchhoff index and the regular Laplace eigenvalue and matrix decomposition theorem.<\/jats:p>","DOI":"10.3390\/sym15051122","type":"journal-article","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T01:31:46Z","timestamp":1684719106000},"page":"1122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Kirchhoff Index and Degree Kirchhoff Index of Tetrahedrane-Derived Compounds"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0667-0478","authenticated-orcid":false,"given":"Duoduo","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Big Data and Artificial Intelligence, Chizhou University, Chizhou 247000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Big Data and Artificial Intelligence, Chizhou University, Chizhou 247000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Big Data and Artificial Intelligence, Chizhou University, Chizhou 247000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Big Data and Artificial Intelligence, Chizhou University, Chizhou 247000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1714-1136","authenticated-orcid":false,"given":"Kai","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Big Data and Artificial Intelligence, Chizhou University, Chizhou 247000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF01164627","article-title":"Resistance distance","volume":"12","author":"Klein","year":"1993","journal-title":"J. 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