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شناسایی نودهای با مرکزیت بالا در شبکه های اجتماعی
ماهیار، حمید رضا Mahyar, Hamid Reza

Cataloging brief

شناسایی نودهای با مرکزیت بالا در شبکه های اجتماعی
پدیدآور اصلی :   ماهیار، حمید رضا Mahyar, Hamid Reza
ناشر :   صنعتی شریف
سال انتشار  :   1396
موضوع ها :   شبکه های اجتماعی Social Networks ضریب خوشه بندی Clustering Coefficient بازسازی تنک...
شماره راهنما :   ‭52-50771

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  • Author's Declaration
  • Abstract (3)
  • Acknowledgements (5)
  • Dedication (6)
  • List of Figures (7)
  • List of Tables (15)
  • Introduction (17)
    • Background and Motivations (17)
    • Network Centrality Measures (19)
    • Problem Statement (23)
  • Compressive Sensing Framework for Sparse Recovery in Networks (27)
    • Sparsity Property of Central Nodes in Networks (27)
    • Compressive Sensing (30)
    • Compressive Sensing over Networks (34)
  • Literature Review (37)
    • Related Work on Network Centrality (37)
    • Related Work on Detection of Central Nodes in Networks (43)
    • Related Work on Measurement Matrix Construction in Networks (46)
  • A Column-wise Measurement Matrix Construction for Identification of Central Nodes in Social Networks (69)
    • Introduction (69)
    • The Proposed Method: DICeNod (70)
      • Complexity Analysis of DICeNod (75)
    • Theoretical Analysis (77)
      • Minimum Sufficient Measurements in DICeNod (77)
      • Satisfying Network Topological Constraint (81)
      • Recovery Guarantees (85)
    • Experimental Evaluation (86)
      • Datasets (86)
      • Competing Methods (87)
      • Accuracy of DICeNod on Identifying Top-k Central nodes (91)
      • Speedup of DICeNod (92)
        • Methodology (92)
        • Evaluation Results (93)
      • Effectiveness of DICeNod (94)
        • Settings (94)
        • Evaluation Results (95)
      • Correlation between Local and Global betweenness Centralities (97)
        • settings (97)
        • Evaluation Results (98)
    • Conclusion (100)
  • A Row-wise Measurement Matrix Construction Method for Detection of Central Nodes in Networks (102)
    • Introduction (102)
    • The Proposed Method (103)
    • Complexity Analysis (110)
    • Experimental Evaluation (113)
      • Datasets (113)
      • Competing Methods (114)
      • Settings (116)
      • Evaluation Results (117)
        • The Accuracy of CS-HiBet on Identifying Top-k Central Nodes (117)
        • The Accuracy of CS-HiBet on Rank Prediction (119)
        • The Effect of Number of Measurements m on the Accuracy of CS-HiBet (122)
        • The Effect of Measurement Length l on the Accuracy of CS-HiBet (122)
        • Recovery Probability (125)
        • The Effect of m and l on the Accuracy of CS-HiCl (125)
    • Conclusion (126)
  • Top-k Centrality Identification and its Application in Community Detection (129)
    • Introduction (129)
    • Problem Importance (131)
    • The Proposed Methods (132)
      • CS-TopCent (132)
        • Complexity Analysis (136)
      • CS-ComDet (137)
    • Experimental Evaluation (140)
      • Evaluation of CS-TopCent (140)
        • Datasets (140)
        • settings (141)
        • Evaluation Results (142)
      • Evaluation of CS-ComDet (148)
        • Datasets (148)
        • settings (149)
        • Evaluation Results (149)
    • Conclusion (152)
  • A Low-cost Sparse Recovery Framework for Weighted Networks (154)
    • Introduction (154)
    • Problem Statement (155)
    • The Proposed Method: LSR-Weighted (156)
    • Complexity Analysis (160)
    • Experimental Evaluation (161)
      • Datasets (161)
      • Settings (162)
      • Evaluation Results (162)
    • Conclusion (166)
  • Conclusions and Future Work (167)
  • Bibliography (171)
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