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BEGIN:VEVENT
DTSTAMP:20260429T181748Z
DTSTART;VALUE=DATE-TIME:20250310T150000
DTEND;VALUE=DATE-TIME:20250310T160000
SUMMARY:Econometrics & Statistics Seminar - Wen Zhou (NYU)
TZID:Europe/London
UID:20250310-8ac672c694ee05350194ef614a8d044e@warwick.ac.uk
CREATED:20250307T092846Z
DESCRIPTION:Title: Identification of Informative Core Structures in Weigh
 ted Directed Networks with Uncertainty Quantification Abstract: In netwo
 rk analysis\, noises and biases\, which are often introduced by peripher
 al or non-essential components\, can mask pivotal structures and hinder 
 the efficacy of many network modeling and inference procedures. Recogniz
 ing this\, identification of the core--periphery (CP) structure has emer
 ged as a crucial data pre-processing step. While the identification of t
 he CP structure has been instrumental in pinpointing core structures wit
 hin networks\, its application to directed weighted networks has been un
 derexplored. Many existing efforts either fail to account for the direct
 ionality or lack the theoretical justification of the identification pro
 cedure. In this work\, we seek answers to three pressing questions: (i) 
 How to distinguish the informative and noninformative structures in weig
 hted directed networks? (ii) What approach offers computational efficien
 cy in discerning these components? (iii) Upon the detection of CP struct
 ure\, can uncertainty be quantified to evaluate the detection? We adopt 
 the signal-plus-noise model\, categorizing different types of noninforma
 tive relational patterns\, by which we define the sender and receiver pe
 ripheries. Furthermore\, instead of confining the core component to a sp
 ecific structure\, we consider it complementary to either the sender or 
 receiver peripheries. Based on our definitions on the sender and receive
 r peripheries\, we propose spectral algorithms to identify the CP struct
 ure in directed weighted networks. Our algorithm stands out with statist
 ical guarantees\, ensuring the identification of sender and receiver per
 ipheries with overwhelming probability. Additionally\, we propose a hypo
 thesis testing framework to infer CP structure upon detection. Our metho
 ds scale effectively for expansive directed networks. Implementing our m
 ethodology on faculty hiring network data revealed captivating insights 
 into the informative structures and distinctions between informative and
  noninformative sender/receiver nodes across various academic discipline
 s. This is a joint work with Wenqin Du\, Tianxi Li\, and Lihua Lei.
LOCATION:S2.79
CATEGORIES:Econometrics Workshop
LAST-MODIFIED:20250307T092846Z
ORGANIZER;CN=Gill Gudger:
END:VEVENT
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