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Inference and Size Localization of Mesoscale Structures in Temporal Networks
Inference and Size Localization of Mesoscale Structures in Temporal Networks
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103620
- ISBN
- 9798315767947
- DDC
- 519
- 서명/저자
- Inference and Size Localization of Mesoscale Structures in Temporal Networks
- 발행사항
- [Sl] : University of California, Los Angeles, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 144 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
- 주기사항
- Advisor: Porter, Mason A.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2025.
- 초록/해제
- 요약In studies of networks, researchers often examine the evolution of mesoscale structures, structures that involve groups of nodes that are larger than a single node but smaller than an overall network. A prominent approach to studying such structures is statistical inference. In the present thesis, we use statistical-inference methods to detect two such mesoscale structures, community structure and core-periphery structure, in time-dependent networks (i.e., "temporal networks"). We represent temporal networks as multilayer networks, with each layer encoding a time step, and we devise statistical-inference methods that avoid common biases in such methods against generating communities or other groups with large or small numbers of nodes. We show that our methods are able to accurately identify mesoscale structure in cases of interest. Additionally, we show that using our generative model is beneficial for analyzing the community structure of networks with large or small communities. It leads to better accuracy than methods that contain biases against generating groups with large or small numbers of nodes. We also generalize hierarchical core-periphery structure, which is a type of core-periphery structure in which nodes can be members of multiple groups simultaneously, to temporal networks. We use a statistical-inference approach to identify such core-periphery structure in real-world temporal networks.
- 일반주제명
- Applied mathematics
- 일반주제명
- Computer engineering
- 일반주제명
- Statistics
- 키워드
- Generative model
- 기타저자
- University of California, Los Angeles Mathematics 0540
- 기본자료저록
- Dissertations Abstracts International. 86-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798315767947
■035 ▼a(MiAaPQ)AAI32045298
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519
■1001 ▼aFaust, Theodore Yushin.
■24510▼aInference and Size Localization of Mesoscale Structures in Temporal Networks
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a144 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-11, Section: B.
■500 ▼aAdvisor: Porter, Mason A.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2025.
■520 ▼aIn studies of networks, researchers often examine the evolution of mesoscale structures, structures that involve groups of nodes that are larger than a single node but smaller than an overall network. A prominent approach to studying such structures is statistical inference. In the present thesis, we use statistical-inference methods to detect two such mesoscale structures, community structure and core-periphery structure, in time-dependent networks (i.e., "temporal networks"). We represent temporal networks as multilayer networks, with each layer encoding a time step, and we devise statistical-inference methods that avoid common biases in such methods against generating communities or other groups with large or small numbers of nodes. We show that our methods are able to accurately identify mesoscale structure in cases of interest. Additionally, we show that using our generative model is beneficial for analyzing the community structure of networks with large or small communities. It leads to better accuracy than methods that contain biases against generating groups with large or small numbers of nodes. We also generalize hierarchical core-periphery structure, which is a type of core-periphery structure in which nodes can be members of multiple groups simultaneously, to temporal networks. We use a statistical-inference approach to identify such core-periphery structure in real-world temporal networks.
■590 ▼aSchool code: 0031.
■650 4▼aApplied mathematics
■650 4▼aComputer engineering
■650 4▼aStatistics
■653 ▼aTemporal networks
■653 ▼aMesoscale structures
■653 ▼aCore-periphery structure
■653 ▼aGenerative model
■653 ▼aStatistical inference
■690 ▼a0364
■690 ▼a0464
■690 ▼a0463
■71020▼aUniversity of California, Los Angeles▼bMathematics 0540.
■7730 ▼tDissertations Abstracts International▼g86-11B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357933▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
