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Network role mining and analysis [electronic resource]

Network role mining and analysis [electronic resource]

자료유형
E-Book(소장)
개인저자
Doran, Derek.
서명 / 저자사항
Network role mining and analysis [electronic resource] / Derek Doran.
발행사항
Cham :   Springer,   c2017.  
형태사항
1 online resource (xi, 101 p.) : ill. (some col.).
총서사항
SpringerBriefs in complexity,2191-5326, 2191-5334 (electronic)
ISBN
9783319538860 (e-book) 9783319538853
요약
This brief presents readers with a summary of classic, modern, and state-of-the-art methods for discovering the roles of entities in networks (including social networks) that range from small to large-scale. It classifies methods by their mathematical underpinning, whether they are driven by implications about entity behaviors in system, or if they are purely data driven. The brief also discusses when and how each method should be applied, and discusses some outstanding challenges toward the development of future role mining methods of each type.
일반주기
Title from e-Book title page.  
내용주기
Network Role Mining and Analysis: An Overview -- Implied Role Mining -- Equivalence-Based Role Mining -- Deterministic Blockmodeling -- Stochastic Blockmodeling -- Advanced Computational Methods -- Concluding Remarks.
서지주기
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Data mining. Big data. Consciousness.
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300 ▼a 1 online resource (xi, 101 p.) : ▼b ill. (some col.).
490 1 ▼a SpringerBriefs in complexity, ▼x 2191-5326, ▼x 2191-5334 (electronic)
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
505 0 ▼a Network Role Mining and Analysis: An Overview -- Implied Role Mining -- Equivalence-Based Role Mining -- Deterministic Blockmodeling -- Stochastic Blockmodeling -- Advanced Computational Methods -- Concluding Remarks.
520 ▼a This brief presents readers with a summary of classic, modern, and state-of-the-art methods for discovering the roles of entities in networks (including social networks) that range from small to large-scale. It classifies methods by their mathematical underpinning, whether they are driven by implications about entity behaviors in system, or if they are purely data driven. The brief also discusses when and how each method should be applied, and discusses some outstanding challenges toward the development of future role mining methods of each type.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Data mining.
650 0 ▼a Big data.
650 0 ▼a Consciousness.
830 0 ▼a SpringerBriefs in complexity.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-3-319-53886-0
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 006.312 등록번호 E14018610 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

컨텐츠정보

목차

CONTENTS
1 Network Role Mining and Analysis : An Overview = 1
 1.1 Introduction = 1
 1.2 Denning Roles = 2
  1.2.1 Networks = 3
  1.2.2 Positions in Networks = 5
 1.3 Mining Roles = 6
  1.3.1 Relationship to Graph Partitioning and Community Detection = 10
 1.4 Purpose and Outline of This Monograph = 11
 References = 11
2 Implied Role Mining = 15
 2.1 Introduction = 15
 2.2 The Implied Role Mining Process = 17
 2.3 Illustrations with Usenet = 18
  2.3.1 Golder et al.''''s Taxonomy = 19
  2.3.2 Nolker et al.''''s Hierarchy = 22
 2.4 Analysis of Implied Role Mining = 25
  2.4.1 Qualitative Nature = 25
  2.4.2 Compatibility = 26
  2.4.3 Simplicity and Interpretability = 27
 2.5 Conclusion = 29
 References = 30
3 Equivalence-Based Role Mining = 31
 3.1 Introduction = 31
 3.2 Structural Equivalence = 32
  3.2.1 Finding Structural Equivalences = 32
 3.3 Automorphic Equivalence = 34
  3.3.1 Finding Automorphic Equivalences = 35
  3.3.2 Quantifying Automorphic Similarity = 37
 3.4 Regular Equivalence = 40
  3.4.1 Finding Regular Equivalences = 40
  3.4.2 Quantifying Regular Similarity = 45
 3.5 Conclusion = 46
 References = 46
4 Deterministic Blockmodeling = 49
 4.1 Introduction = 49
 4.2 The Blockmodeling Framework = 52
  4.2.1 Similarity Measures = 52
  4.2.2 Blocktypes = 56
 4.3 Goodness of Fit = 58
  4.3.1 A Goodness-of-Fit Measure for Positional Analysis = 59
  4.3.2 A Goodness-of-Fit Measure for Network Compression = 60
 4.4 Conclusion = 60
 References = 61
5 Stochastic Blockmodeling = 63
 5.1 Introduction = 63
 5.2 SBM Specification = 64
 5.3 The Infinite Relational Model = 65
  5.3.1 Parameter Inference for the IRM = 67
  5.3.2 Summary = 70
 5.4 The Dynamic Stochastic Blockmodel = 70
  5.4.1 DSBM Network Generation = 71
  5.4.2 Parameter Inference for the DSBM = 73
 5.5 Conclusion = 75
 References = 75
6 Advanced Computational Methods = 77
 6.1 Factor Graphs : The Social Roles and Statuses Factor Graph Model = 78
  6.1.1 Social Features = 79
  6.1.2 A Factor Graph Model = 80
 6.2 Multi-view Learning : Dual-View Uncertainty Regularization = 82
  6.2.1 Graph Co-regularization = 83
  6.2.2 Uncertainty Regularization and Objective Function = 84
 6.3 Bayesian Modeling : Co-discovery of Roles in Communities = 85
 6.4 Matrix Factorization : RolX = 87
 6.5 Iterative Quadratic Programming : Synergistic Co-discovery of Communities and Roles = 89
  6.5.1 Initializing Communities (InitCom) = 90
  6.5.2 Initializing Roles (InitRole) = 90
  6.5.3 Updating Communities = 91
  6.5.4 Updating Roles = 92
 6.6 Conclusion = 92
 References = 93
7 Concluding Remarks = 95
 7.1 Emerging Trends in Role Mining = 96
 7.2 Tension Between Rigor and Interpretability = 98
 References = 100

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