A0165
Title: Detection and statistical inference on informative core and periphery structures in weighted directed networks
Authors: Wen Zhou - New York University (United States) [presenting]
Tianxi Li - University of Minnesota (United States)
Wenqin Du - University of Southern California (United States)
Abstract: In network analysis, noises and biases due to peripheral or non-essential components can mask pivotal structures and hinder the efficacy of many network modelling and inference procedures. Recognizing this, the identification of the core-periphery (CP) structure has emerged as a crucial data pre-processing step, while the efforts to detect CP for directed weighted networks have been limited. Existing efforts either fail to account for the directionality or lack the theoretical justification of the identification procedure. The aim is to answer three pressing questions: (i) How can informative and non-informative structures in weighted directed networks be distinguished? (ii) What approach offers computational efficiency in discerning these components? (iii) Upon the detection of CP structure, can uncertainty be quantified to evaluate the detection? The signal-plus-noise model is adopted, categorizing uniform relational patterns as non-informative, by which we define the sender and receiver peripheries. Furthermore, instead of confining the core component to a specific structure, it is considered complementary to either the sender or receiver peripheries. Based on the definitions of the sender and receiver peripheries, spectral algorithms are proposed to identify the CP structure in directed weighted networks. The algorithm stands out with statistical guarantees, ensuring the identification of sender and receiver peripheries with overwhelming probability.
