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UID:calendar.313236.field_event_date.0@www.wright.edu
DTSTAMP:20260219T225800Z
CREATED:20180405T192551Z
DESCRIPTION:Committee:  Drs. Derek Doran\, Advisor\, Tanvi Banerjee\, and F
 red Garber (Electrical Engineering)ABSTRACT:Sociotechnological and geospat
 ial processes exhibit time varying structure that make insight discovery c
 hallenging. This thesis proposes a new statistical model for such systems\
 , modeled as dynamic networks\, to address this challenge. It assumes that
  vertices fall into one of k types and that the probability of edge format
 ion at a particular time depends on the types of the incident nodes and th
 e current time. The time dependencies are driven by unique seasonal proces
 ses\, which many systems exhibit (e.g.\, predictable spikes in geospatial 
 or web traffic each day). The thesis defines the model as a generative pro
 cess and an inference procedure to recover the `normal' seasonal processes
  from data when they are unknown.Evaluation is completed on several synthe
 tic and real datasets. The synthetic experiments demonstrate the superior 
 fidelity of this model on seasonal datasets\, while being able to remain e
 qually accurate for non-seasonal data. The model is up to twice as accurat
 e at predicting future edge density over competing models on New York City
  Taxi trips\, United States airline flights\, and email communication with
 in the Enron company. A software tool named GEONET is developed for anomal
 y detection and exploration by a human analyst on geospatial data and is u
 tilized on the New York City data.
DTSTART;TZID=America/New_York:20180409T110000
DTEND;TZID=America/New_York:20180409T130000
LAST-MODIFIED:20180405T201923Z
LOCATION:304 Russ Engineering
SUMMARY:Masters Thesis Defense “Seasonality in Dynamic Stochastic Blockmode
 ls” By Jace Robinson
URL;TYPE=URI:/events/masters-thesis-defense-%E2%80%9C
 seasonality-dynamic-stochastic-blockmodels%E2%80%9D-jace-robinson
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