A Stochastic System for Large Network Growth

Autor:	Nadya T. Bliss, Benjamin A. Miller
Rok vydání:	2012
Předmět:	Mathematical optimization Degree (graph theory) Stochastic modelling Applied Mathematics Process (computing) Graph theory Context (language use) Preferential attachment Data modeling Noise Signal Processing Electrical and Electronic Engineering Algorithm Mathematics
Zdroj:	IEEE Signal Processing Letters. 19:356-359
ISSN:	1558-2361 1070-9908
DOI:	10.1109/lsp.2012.2195312
Popis:	This letter proposes a new model for preferential attachment in dynamic directed networks. This model consists of a linear time-invariant system that uses past observations to predict future attachment rates, and an innovation noise process that induces growth on vertices that previously had no attachments. Analyzing a large citation network in this context, we show that the proposed model fits the data better than existing preferential attachment models. An analysis of the noise in the dataset reveals power-law degree distributions often seen in large networks, and polynomial decay with respect to age in the probability of citing yet-uncited documents.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e8d247d74cb023cee925109cd1743930 https://doi.org/10.1109/lsp.2012.2195312 Zobrazit plný text záznamu