자료유형  

 학위논문

Control Number  

0017162419

International Standard Book Number  

9798382825434

Dewey Decimal Classification Number  

310

Main Entry-Personal Name  

Zhang, Shichang.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Los Angeles., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

192 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: A.

General Note  

Advisor: Sun, Yizhou.

Dissertation Note  

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

Summary, Etc.  

요약The development of artificial intelligence (AI) has significantly impacted our daily lives and even driven new scientific discoveries. However, the modern AI models based on deep learning remain opaque "black boxes'' and raise a critical "why question'' - why are these AI models capable of achieving such remarkable outcomes? Answering this question leads to research on Explainable AI (XAI), which offers numerous benefits, such as enhancing model performance, establishing user trust, and extracting deeper insights from data. While XAI has been explored for some data modalities like images and text, relevant research on graph data, a more complex data modality that represents both entities and their relationships, is underdeveloped. Given the ubiquity of graph data and their prevalent applications across main domains including science, business, and healthcare, XAI for graph data becomes a critical research direction.This thesis aims to address the gap in XAI for graph data from three complementary and equally important perspectives: model, user, and data. Accordingly, my research advances XAI for graph data by developing: (1) Model-oriented explanation techniques that illuminate the mechanism and enhance the performance of state-of-the-art AI models on graph data. (2) User-oriented explanation approaches that offer intuitive visualizations and natural language explanations to establish user trust in graph AI models for real-world applications. (3) Data-oriented explanation methods that identify key patterns and extract insights from graph data, potentially leading to new scientific discoveries. By integrating these three perspectives, this thesis enhances the transparency, trustworthiness, and insightfulness of AI for graph data across domains and applications.

Subject Added Entry-Topical Term  

Statistics.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Information science.

Index Term-Uncontrolled  

Graph Data

Index Term-Uncontrolled  

Black boxes

Index Term-Uncontrolled  

Graph neural networks

Index Term-Uncontrolled  

Cooperative game theory

Index Term-Uncontrolled  

Explainable AI

Added Entry-Corporate Name  

University of California, Los Angeles Computer Science 0201

Host Item Entry  

Dissertations Abstracts International. 85-12A.

Electronic Location and Access  

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Control Number  

joongbu:657197