A Theoretical Study on Event Spreading Prediction by Probabilistic Connectivity Analysis in Dispersive Networks
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study discusses a potential use of probabilistic connectivity analysis for prediction of future progresses of events in stochastic network models. Probabilistic relation graphs provide a useful mathematical tool for representation of stochastic network models such as Markov chain models and random transitive networks. These stochastic network models have been widely and effectively used for analysis purpose in a range of application areas (e.g. statistics, language processing, genetics...). In this fashion, the current study investigates applications of the graph connectivity analysis based on taking powers of probabilistic relation matrices. Such probabilistic connectivity analysis can provide knowledge for prediction of future progress of spreading stochastic events in dispersive networks. Some properties of a stochastic dispersive network can be explored by taking power of probabilistic relation matrix, which indeed yields a probabilistic projection for future progress of the network in probability domain. Paper aims to develop a basic understanding for applications of probabilistic connectivity analysis for dispersive networks. Accordingly, applications of this analysis are considered, and illustrative examples are presented for discussion.
Açıklama
International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 21-22, 2019 -- Inonu Univ, Malatya, TURKEY
Anahtar Kelimeler
Prediction, Markov chain, transitive networks, graph connectivity, probabilistic relation, matrix power
Kaynak
2019 International Conference on Artificial Intelligence and Data Processing (Idap 2019)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
