• 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)