TY - JOUR
T1 - On graph entropy measures based on the number of independent sets and matchings
AU - Wan, Pengfei
AU - Chen, Xinzhuang
AU - Tu, Jianhua
AU - Dehmer, Matthias
AU - Zhang, Shenggui
AU - Emmert-Streib, Frank
PY - 2020/4
Y1 - 2020/4
N2 - In this paper, we consider the graph entropy measures based on the number of independent sets and matchings. The reason to study these measures relates to the fact that the independent set and matching problem is computationally demanding. However, these features can be calculated for smaller networks. In case one can establish efficient estimations, those measures may be also used for larger graphs. So, we establish some upper and lower bounds as well as some information inequalities for these information-theoretic quantities. In order to give further evidence, we also generate numerical results to study these measures such as list the extremal graphs for these entropies. Those results reveal the two entropies possess some new features.
AB - In this paper, we consider the graph entropy measures based on the number of independent sets and matchings. The reason to study these measures relates to the fact that the independent set and matching problem is computationally demanding. However, these features can be calculated for smaller networks. In case one can establish efficient estimations, those measures may be also used for larger graphs. So, we establish some upper and lower bounds as well as some information inequalities for these information-theoretic quantities. In order to give further evidence, we also generate numerical results to study these measures such as list the extremal graphs for these entropies. Those results reveal the two entropies possess some new features.
KW - Graph entropy measures
KW - Independent set
KW - Matching
KW - Quantitative graph theory
KW - Subgraph
UR - http://www.scopus.com/inward/record.url?scp=85077433456&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2019.11.020
DO - 10.1016/j.ins.2019.11.020
M3 - Article
AN - SCOPUS:85077433456
SN - 0020-0255
VL - 516
SP - 491
EP - 504
JO - Information Sciences
JF - Information Sciences
ER -