Multi-facet graph mining with contextualized projections

The goal of my doctoral research is to develop a new generation of graph mining techniques, centered around my proposed idea of multi-facet contextualized projections, for more systematic, flexible, and scalable knowledge discovery around massive, complex, and noisy real-world context-rich networks across various domains. Traditional graph theories largely overlook network contexts, whereas state-of-the-art graph mining algorithms simply regard them as associative attributes and brutally employ machine learning models developed in individual domains (e.g., convolutional neural networks in computer vision, recurrent neural networks in natural language processing) to handle them jointly. As such, essentially different contexts (e.g., temporal, spatial, textual, visual) are mixed up in a messy, unstable, and uninterpretable way, while the correlations between graph topologies and contexts remain a mystery, which further renders the development of real-world mining systems less principled and ineffective. To overcome such barriers, my research harnesses the power of multi-facet context modeling and focuses on the principle of contextualized projections, which provides generic but subtle solutions to knowledge discovery over graphs with the mixtures of various semantic contexts.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Yang, Carl

Contributors dc:contributor

• Han, Jiawei
• Peng, Jian
• Zhai, ChengXiang
• Leskovec, Jure

• Graph mining
• Contextualized projection
• Graph embedding
• Graph generation
• Network applications

Statement dc:rights

• Copyright 2020 Ji Yang (Carl)

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/109388

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/109388