ΕΚΔΗΛΩΣΕΙΣ

ΣΕΜΙΝΑΡΙΟ ΣΤΑΤΙΣΤΙΚΗΣ ΚΑΙ ΕΠΙΧΕΙΡΗΣΙΑΚΗΣ ΕΡΕΥΝΑΣ: Κ. ΠΑΝΤΑΖΗΣ

Σεμινάριο Στατιστικής και Επιχειρησιακής Έρευνας: Κ. Πανταζής

Ομιλητής:  Κωνσταντίνος Πανταζής, PhD Candidate, University of Maryland College Park

Ημερομηνία: Παρασκευή 10/12/2021, 12:00 μμ

Αίθουσα Α32 Τμήμα Μαθηματικών

Διαδικτυακή Μετάδοση: https://uoa.webex.com/meet/aburnetas

Τίτλος:Στατιστική συμπερασματολογία σε πολλαπλά τυχαία γραφήματα  - Statistical Inference on Multiple Random Graphs

Περίληψη:

Performing statistical analyses on collections of graphs is of import to many disciplines, but principled, scalable methods for multi-sample graph inference are few. Recent work has demonstrated that joint, or simultaneous, spectral embedding of multiple independent networks can deliver more accurate estimation than individual spectral decompositions of those same networks. Such inference procedures typically rely heavily on independence assumptions across the multiple network realizations, and even in this case, little attention has been paid to the induced network correlation that can be a consequence of such joint embeddings. We present a generalized omnibus embedding methodology and we provide a detailed analysis of this embedding across both independent and correlated networks, the latter of which significantly extends the reach of such procedures, and we describe how this omnibus embedding can itself induce correlation.
This leads us to distinguish between inherent correlation---that is, the correlation that arises naturally in multisample network data---and induced correlation, which is an artifice of the joint embedding methodology. We show that the generalized omnibus embedding procedure is flexible and robust, and we prove both consistency and a central limit theorem for the embedded points.
Further, we show how an appropriately calibrated generalized omnibus embedding can detect changes in real biological networks that previous embedding procedures could not discern, confirming that the effect of inherent and induced correlation can be subtle and transformative.