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Explainable Artificial Intelligence for Graph Data
Explainable Artificial Intelligence for Graph Data
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152010
- ISBN
- 9798382825434
- DDC
- 310
- 저자명
- Zhang, Shichang.
- 서명/저자
- Explainable Artificial Intelligence for Graph Data
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 192 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: A.
- 주기사항
- Advisor: Sun, Yizhou.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 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.
- 일반주제명
- Statistics
- 일반주제명
- Computer science
- 일반주제명
- Information science
- 키워드
- Graph Data
- 키워드
- Black boxes
- 키워드
- Explainable AI
- 기타저자
- University of California, Los Angeles Computer Science 0201
- 기본자료저록
- Dissertations Abstracts International. 85-12A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152010
■006m o d
■007cr#unu||||||||
■020 ▼a9798382825434
■035 ▼a(MiAaPQ)AAI31331047
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aZhang, Shichang.
■24510▼aExplainable Artificial Intelligence for Graph Data
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a192 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: A.
■500 ▼aAdvisor: Sun, Yizhou.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aThe 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.
■590 ▼aSchool code: 0031.
■650 4▼aStatistics
■650 4▼aComputer science
■650 4▼aInformation science
■653 ▼aGraph Data
■653 ▼aBlack boxes
■653 ▼aGraph neural networks
■653 ▼aCooperative game theory
■653 ▼aExplainable AI
■690 ▼a0800
■690 ▼a0984
■690 ▼a0723
■690 ▼a0463
■71020▼aUniversity of California, Los Angeles▼bComputer Science 0201.
■7730 ▼tDissertations Abstracts International▼g85-12A.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162419▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
