文献类型：期刊文献

英文题名：Signless Laplacian energy and spectral radius of a graph

作者：Chen, Yuanyuan[1];Liu, Shuting[2];Wang, Zhiwen[3]

机构：[1]Xinjiang Univ, Coll Math & Syst Sci, Urumqi 830017, Xinjiang, Peoples R China;[2]Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Shandong, Peoples R China;[3]East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China

年份：2026

卷号：378

起止页码：216

外文期刊名：DISCRETE APPLIED MATHEMATICS

收录：;EI(收录号：20253018853416);WOS:【SCI-EXPANDED(收录号:WOS:001552056100001)】；

基金：This research is supported by National Natural Science Foundation of China (Grant Nos. 12301438 and 12326373), Natural Science Foundation of Xinjiang Uygur Autonomous Region (No. 2023D01C165), Tianshan Talent Training Program (No. 2024TSYCQNTJ0001) and the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission (No. 23CGA37).

语种：英文

外文关键词：Signless Laplacian energy; Spectral radius; Graph

摘要：For a graph G, the signless Laplacian energy is defined as EQ(G) = Sigma n |qi-2m |, i=1 n where n and m are the order and the size, and qi is the ith largest signless Laplacian eigenvalue of G, respectively. In this paper, we establish a sharp lower bound of the signless Laplacian spectral radius of a graph G in terms of the degree and the average 2-degree, generalizing a well-known lower bound that q1 >= triangle + 1. As applications, we give sharp lower and upper bounds of its signless Laplacian energy for a connected graph and a bipartite graph. All extremal graphs involved are characterized. (c) 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.