Randomizing Hypergraphs Preserving Two-mode Clustering Coefficient

被引：3
|
作者：
Miyashita, Rikuya [1 ]
Nakajima, Kazuki [1 ]
Fukuda, Mei [1 ]
Shudo, Kazuyuki [1 ]
机构：
[1] Tokyo Inst Technol, Tokyo, Japan
来源：
2023 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING, BIGCOMP | 2023年
关键词：
hypergraph; clustering coefficient; generative model;
D O I：
10.1109/BigComp57234.2023.00064
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Hypergraphs are data structures that capture the interactions between two or more nodes. By comparing random hypergraphs that preserve particular properties with an original hypergraph, we can analyze the impacts of those properties. In this study, we propose a method for generating random hypergraphs that preserve the pairwise joint degree distribution and the two-mode clustering coefficient. By the proposed method, we generated hypergraphs that preserve the average degree of the node and the degree of each node and approximately preserve the pairwise joint degree distribution and the two-mode clustering coefficient.
引用
收藏
页码：316 / 317
页数：2
相关论文
共 50 条
  • [1] Random Hypergraph Model Preserving Two-Mode Clustering Coefficient
    Miyashita, Rikuya
    Nakajima, Kazuki
    Fukuda, Mei
    Shudo, Kazuyuki
    BIG DATA ANALYTICS AND KNOWLEDGE DISCOVERY, DAWAK 2023, 2023, 14148 : 191 - 196
  • [2] Randomizing Hypergraphs Preserving Degree Correlation and Local Clustering
    Nakajima, Kazuki
    Shudo, Kazuyuki
    Masuda, Naoki
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2022, 9 (03): : 1139 - 1153
  • [3] Two-mode clustering with genetic algorithms
    Hansohm, J
    CLASSIFICATION, AUTOMATION, AND NEW MEDIA, 2002, : 87 - 93
  • [4] Maximal Interaction Two-Mode Clustering
    Jan Schepers
    Hans-Hermann Bock
    Iven Van Mechelen
    Journal of Classification, 2017, 34 : 49 - 75
  • [5] Maximal Interaction Two-Mode Clustering
    Schepers, Jan
    Bock, Hans-Hermann
    Van Mechelen, Iven
    JOURNAL OF CLASSIFICATION, 2017, 34 (01) : 49 - 75
  • [6] Recurrence properties in two-mode hierarchical clustering
    Castillo, W
    Trejos, J
    CLASSIFICATION AND INFORMATION PROCESSING AT THE TURN OF THE MILLENNIUM, 2000, : 68 - 73
  • [7] Two-mode clustering methods: Compare and contrast
    Krolak-Schwerdt, S
    BETWEEN DATA SCIENCE AND APPLIED DATA ANALYSIS, 2003, : 270 - 278
  • [8] Two-mode clustering methods: a structured overview
    Van Mechelen, I
    Bock, HH
    De Boeck, P
    STATISTICAL METHODS IN MEDICAL RESEARCH, 2004, 13 (05) : 363 - 394
  • [9] Measurement of the differential dispersion coefficient in two-mode fibers
    Langli, B
    Blotekjaer, K
    JOURNAL OF LIGHTWAVE TECHNOLOGY, 2000, 18 (12) : 2123 - 2126
  • [10] Cluster Validity Measures for Fuzzy Two-Mode Clustering
    Ferraro, Maria Brigida
    Giordani, Paolo
    Vichi, Maurizio
    BUILDING BRIDGES BETWEEN SOFT AND STATISTICAL METHODOLOGIES FOR DATA SCIENCE, 2023, 1433 : 144 - 150
12345