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Title:Multi-facet graph mining with contextualized projections
Author(s):Yang, Carl
Director of Research:Han, Jiawei
Doctoral Committee Chair(s):Han, Jiawei
Doctoral Committee Member(s):Peng, Jian; Zhai, ChengXiang; Leskovec, Jure
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Graph mining
Contextualized projection
Graph embedding
Graph generation
Network applications
Abstract: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.
Issue Date:2020-12-01
Type:Thesis
URI:http://hdl.handle.net/2142/109388
Rights Information:Copyright 2020 Ji Yang (Carl)
Date Available in IDEALS:2021-03-05
Date Deposited:2020-12


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