Modeling and Inferring Attributed Graphs

Jia, Junteng

Modeling and Inferring Attributed Graphs

Abstract

Graphs are a natural representation for systems with interacting components (e.g. an online social network of users; a transaction network of bank accounts; an interaction network of proteins). As such, algorithms that predict node labels have wide-ranged applications from online content recommendation, fraud detection, to drug discovery. The traditional machine learning setting assumes data points are independently sampled, and thus makes predictions only based on each individual’s attributes. For interconnected vertices in an attributed graph, the correlation along the edges provide an additional source of information. To better understand and leverage those two types of information, we propose data models for attributed graphs that: (1) explain existing graph learning algorithms such as label propagation and graph convolutional network, (2) inspire new algorithms that achieves the state-of-the-art performances, (3) generate synthetic graph attributes that preserves characteristics in real-world data.

Description

142 pages

Date Issued

2021-05

Keywords

Attributed Graphs; Belief Propagation; Graph Neural Networks; Label Propagation; Markov Random Field

Committee Chair

Benson, Austin Reilley

Committee Member

Bindel, David S.
Kleinberg, Jon M.

Degree Discipline

Computer Science

Degree Name

Ph. D., Computer Science

Degree Level

Doctor of Philosophy

Rights

Attribution 4.0 International

Rights URI

https://creativecommons.org/licenses/by/4.0/

Types

dissertation or thesis