Explore Statistical Properties of Undirected Unweighted Networks from Ensemble Models

Xunda Zhao, Xing Wu, Jianjia Wang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Complex network theory has been widely demonstrated as a powerful tool in modeling and characterizing various complex systems. In the past, complex network theory has focused on the behaviors as well as the characteristics of the network nodes and edges. However, with the continuous evolution of society, traditional graph theory faces challenges due to the emergence of extermely large network structures. Recently, complex network method based statistics has attracted much attention. The new approach effectively manages very large networks and uncovers their intrinsic properties. In this paper, we present a complex network analysis model for undirected, unweighted networks based on a statistical analysis approach. This model is inspired by the ensemble model in thermostatistical physics. Based on the established mathematical model, we derive physical measures that reflect the intrinsic properties of the network, including Entropy, Free Energy, Temperature, and so on. In the experimental part, we first explored the mathematical characterization of these metrics. Then, we observed the performance of various network categories under the same metric. Finally, we applied these measures to the field of graph classification. Extensive experiments demonstrate the effectiveness and superiority of the proposed method.

Original languageEnglish
Title of host publicationPattern Recognition - 27th International Conference, ICPR 2024, Proceedings
EditorsApostolos Antonacopoulos, Subhasis Chaudhuri, Rama Chellappa, Cheng-Lin Liu, Saumik Bhattacharya, Umapada Pal
PublisherSpringer Science and Business Media Deutschland GmbH
Pages131-145
Number of pages15
ISBN (Print)9783031783975
DOIs
Publication statusPublished - 2025
Event27th International Conference on Pattern Recognition, ICPR 2024 - Kolkata, India
Duration: 1 Dec 20245 Dec 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15327 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Pattern Recognition, ICPR 2024
Country/TerritoryIndia
CityKolkata
Period1/12/245/12/24

Keywords

  • complex network
  • ensemble
  • entropy
  • statistical mechanics

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