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Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/51823 Cómo citar
Título: Change point detection in weighted and directed random dot product graphs
Autor: Larroca, Federico
Bermolen, Paola
Fiori, Marcelo
Mateos, Gonzalo
Tipo: Ponencia
Palabras clave: Change-point detection, Graph representation learning, Node embeddings, Wireless networks
Fecha de publicación: 2021
Resumen: Given a sequence of possibly correlated randomly generated graphs, we address the problem of detecting changes on their underlying distribution. To this end, we will consider Random Dot Product Graphs (RDPGs), a simple yet rich family of random graphs that subsume Erd¨os-R´enyi and Stochastic Block Model ensembles as particular cases. In RDPGs each node has an associated latent vector and inner products between these vectors dictate the edge existence probabilities. Previous works have mostly focused on the undirected and unweighted graph case, a gap we aim to close here. We first extend the RDPG model to accommodate directed and weighted graphs, a contribution whose interest transcends change-point detection (CPD). A statistic derived from the nodes’ estimated latent vectors (i.e., embeddings) facilitates adoption of scalable geometric CPD techniques. The resulting algorithm yields interpretable results and facilitates pinpointing which (and when) nodes are acting differently. Numerical tests on simulated data as well as on a real dataset of graphs stemming from a Wi-Fi network corroborate the effectiveness of the proposed CPD method.
Editorial: EUSIPCO
EN: 2021 29th European Signal Processing Conference (EUSIPCO), Dublin, Ireland, 23-27 aug. 2021, pp. 1-5.
Financiadores: ANII Beca FMV 3 2018 1 148149
NSF Beca CCF-1750428 y ECCS-1809356
Citación: Larroca, F., Bermolen, P., Fiori, M. y otros. Change point detection in weighted and directed random dot product graphs [en línea]. EN: 2021 29th European Signal Processing Conference (EUSIPCO), Dublin, Ireland, 23-27 aug. 2021, pp. 1-5.
Departamento académico: Telecomunicaciones
Grupo de investigación: Análisis de Redes, Tráficos y Estadísticas de Servicios (ARTES)
Licencia: Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Aparece en las colecciones: Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica

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