PurdueThesis_Hao_Sha.pdf

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SOLVING PREDICTION PROBLEMS FROM TEMPORAL EVENT DATA ON NETWORKS

thesis
posted on 06.08.2021, 13:53 by Hao ShaHao Sha

Many complex processes can be viewed as sequential events on a network. In this thesis, we study the interplay between a network and the event sequences on it. We first focus on predicting events on a known network. Examples of such include: modeling retweet cascades, forecasting earthquakes, and tracing the source of a pandemic. In specific, given the network structure, we solve two types of problems - (1) forecasting future events based on the historical events, and (2) identifying the initial event(s) based on some later observations of the dynamics. The inverse problem of inferring the unknown network topology or links, based on the events, is also of great important. Examples along this line include: constructing influence networks among Twitter users from their tweets, soliciting new members to join an event based on their participation history, and recommending positions for job seekers according to their work experience. Following this direction, we study two types of problems - (1) recovering influence networks, and (2) predicting links between a node and a group of nodes, from event sequences.

History

Degree Type

Doctor of Philosophy

Department

Computer Science

Campus location

Indianapolis

Advisor/Supervisor/Committee Chair

George Mohler

Additional Committee Member 2

Mohammad Hasan

Additional Committee Member 3

Murat Dundar

Additional Committee Member 4

Snehasis Mukhopadhyay