國立虎尾科技大學 |

Explainable Artificial Intelligence for Graph Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Explainable Artificial Intelligence for Graph Data./
作者:	Zhang, Shichang.
面頁冊數:	1 online resource (192 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: A.
Contained By:	Dissertations Abstracts International85-12A.
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9798382825434

Explainable Artificial Intelligence for Graph Data.
Zhang, Shichang.

Explainable Artificial Intelligence for Graph Data. - 1 online resource (192 pages)

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

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

Includes bibliographical references

The 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382825434Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

Graph DataIndex Terms--Genre/Form:

554714
Electronic books.

Explainable Artificial Intelligence for Graph Data.
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520 $a The 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.
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