The Number of Ways to Construct a Connected Graph: A Graph-Based Generalization of the Binomial Coefficients

被引:0
|
作者
Khmelnitskaya, Anna [1 ]
van der Laan, Gerard [2 ,3 ]
Talman, Dolf [4 ]
机构
[1] St Petersburg State Univ, Fac Appl Math, Univ Kii Prospekt 35, St Petersburg 198504, Russia
[2] Vrije Univ Amsterdam, Sch Business & Econ, Boelelaan 1105, NL-1081 HV Amsterdam, Netherlands
[3] Vrije Univ Amsterdam, Tinbergen Inst, Boelelaan 1105, NL-1081 HV Amsterdam, Netherlands
[4] Tilburg Univ, Dept Econometr & Operat Res, Warandelaan 2, NL-5037 AB Tilburg, Netherlands
基金
俄罗斯基础研究基金会;
关键词
binomial coefficient; Pascal's triangle; connected graph; Markov chain; centrality measure;
D O I
暂无
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
This paper studies the number of ways a given connected simple graph can be constructed by a sequence of expanding connected subgraphs starting with a given vertex. When the graph is a path on n + 1 vertices, these numbers are exactly the binomial coefficients in row n of Pascal's triangle. Hence, for other connected graphs, these numbers, called the connectivity degrees of the vertices, generalize the binomial coefficients. We show that the connectivity degrees have properties that for paths reduce to well-known properties of the binomial coefficients. We also prove that the connectivity degrees of the vertices in a tree, when normalized to sum up to one, are equal to the steady state probabilities of some Markov chain on the vertices of the graph. Furthermore, on a connected graph the connectivity degrees of its vertices can be seen as a measure of centrality. On the class of trees we provide an axiomatic characterization of this connectivity centrality measure.
引用
收藏
页数:20
相关论文
共 14 条
  • [1] Graph-Based Generalization of Galam Model: Convergence Time and Influential Nodes
    Li, Sining
    Zehmakan, Ahad N.
    PHYSICS, 2023, 5 (04): : 1094 - 1108
  • [2] Classification with Graph-Based Markov Chain
    He, Ping
    Xu, Xiaohua
    NATURE-INSPIRED COMPUTATION AND MACHINE LEARNING, PT II, 2014, 8857 : 310 - 318
  • [3] A realistic graph-based alert correlation system
    Ben Fredj, Ouissem
    SECURITY AND COMMUNICATION NETWORKS, 2015, 8 (15) : 2477 - 2493
  • [5] Cut and pendant vertices and the number of connected induced subgraphs of a graph
    Audace A. V. Dossou-Olory
    European Journal of Mathematics, 2021, 7 : 766 - 792
  • [6] A Probabilistic Graph-Based Method to Improve Recommender System Accuracy
    Joorabloo, Nima
    Jalili, Mandi
    Ren, Yongli
    ENGINEERING APPLICATIONS OF NEURAL NETWORKSX, 2019, 1000 : 151 - 163
  • [7] Connected Graph Cluster-Based Routing Algorithm Based on LEACH
    Huang, Liuhong
    Li, Feng
    2ND INTERNATIONAL SYMPOSIUM ON COMPUTER NETWORK AND MULTIMEDIA TECHNOLOGY (CNMT 2010), VOLS 1 AND 2, 2010, : 23 - 26
  • [8] G-EEGCS: Graph-based optimum electroencephalogram channel selection
    Abdullah
    Faye, Ibrahima
    Yusoff, Mohd Zuki
    Belhaouari, Samir Brahim
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2024, 98
  • [9] OutRank: A graph-based outlier detection framework using random walk
    Moonesinghe, H. D. K.
    Tan, Pang-Ning
    INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, 2008, 17 (01) : 19 - 36
  • [10] CENTRALITY BASED NUMBER OF CLUSTER ESTIMATION IN GRAPH CLUSTERING
    Shamsi, Mahdi
    Beheshti, Soosan
    2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, : 3645 - 3649