LEADER 10732nam 22004813 450 001 9910842288303321 005 20240308080228.0 010 $a3-031-53499-9 035 $a(CKB)30597581400041 035 $a(MiAaPQ)EBC31201073 035 $a(Au-PeEL)EBL31201073 035 $a(EXLCZ)9930597581400041 100 $a20240308d2024 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComplex Networks and Their Applications XII $eProceedings of the Twelfth International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2023, Volume 2 205 $a1st ed. 210 1$aCham :$cSpringer,$d2024. 210 4$d©2024. 215 $a1 online resource (523 pages) 225 1 $aStudies in Computational Intelligence Series ;$vv.1142 311 $a3-031-53498-0 327 $aIntro -- Preface -- Organization and Committees -- Contents -- Community Structure -- Identifying Well-Connected Communities in Real-World and Synthetic Networks -- 1 Introduction -- 2 Results -- 2.1 Initial Observations -- 2.2 Connectivity Modifier -- 2.3 Effect of CM on Clustered Real World Networks -- 2.4 Synthetic LFR Networks -- 3 Discussion -- References -- Bayesian Hierarchical Network Autocorrelation Models for Modeling the Diffusion of Hospital-Level Quality of Care -- 1 Introduction -- 2 Notation and Models -- 2.1 Hierarchical Network Autocorrelation Model -- 2.2 Extended Hierarchical Network Autocorrelation Model -- 2.3 Illustration of Marginal Mean and Variance of Extended Model with Simulated Data -- 3 Bayesian Hierarchical Network Autocorrelation Model and Estimation -- 4 Simulation Study -- 5 The Impact on Patient Quality of Hospitals' Adoption of Robotic Surgery -- 6 Discussion -- References -- Topological Community Detection: A Sheaf-Theoretic Approach -- 1 Introduction -- 2 Background: Sheaves and Social Networks -- 2.1 Sheaves and Sheaf Cohomology -- 2.2 Discourse Sheaves and Opinion Dynamics -- 3 Methods and Experimental Design -- 3.1 Detecting Communities with Constant Sheaves -- 3.2 Convergence of Algorithm 1: Community Detection with Constant Sheaves -- 3.3 Detecting Communities with a Non-constant Sheaf -- 3.4 Deterministic Sheaf Community Detection -- 3.5 Experimental Setup -- 4 Experimental Results -- 5 Discussion -- References -- Does Isolating High-Modularity Communities Prevent Cascading Failure? -- 1 Introduction -- 2 Methods -- 2.1 MSNR -- 2.2 Networks -- 2.3 Dynamics -- 2.4 Quality of Partition -- 3 Results -- 4 Discussion -- References -- Two to Five Truths in Non-negative Matrix Factorization -- 1 Bipartite Laplacian and Other Matrix Scalings -- 2 Computational Results -- 2.1 Three Datasets of Varying Difficulty. 327 $a2.2 From Data to Matrices -- 2.3 Clustering with an NMF and Evaluating Performance -- 3 Discussion -- 4 Related Work -- 5 Conclusions -- References -- Adopting Different Strategies for Improving Local Community Detection: A Comparative Study -- 1 Introduction -- 2 Related Work -- 3 Variants of a Local Community Detection with Seeds-0.5em -- 3.1 Preliminaries and Problem Formulation -- 3.2 Proposed Variants -- 4 Experiments -- 4.1 Experiment Design -- 4.2 Experiments on Synthetic and Real Datasets -- 5 Conclusions and Future Scope -- References -- Pyramid as a Core Structure in Social Networks -- 1 Introduction -- 2 Preliminary -- 3 The Proposed Pyramid Structure -- 4 Empirical Studies and Applications -- 4.1 On the Existence of Large Pyramid -- 4.2 A Novel Structural Feature Revealed by Pyramid -- 4.3 Large Pyramid as a Core Structure -- 5 Conclusion -- References -- Dual Communities Characterize Structural Patterns and Robustness in Leaf Venation Networks -- 1 Introduction -- 2 Dual Graphs of Weighted Spatial Networks -- 3 Communities and Hierarchies in Dual Graphs -- 4 Classification of Leaf Venation Patterns -- 5 Dual Communities and Leaf Robustness -- 6 Conclusion -- References -- Tailoring Benchmark Graphs to Real-World Networks for Improved Prediction of Community Detection Performance -- 1 Introduction -- 2 Background -- 2.1 LFR Benchmark Graphs -- 2.2 nPSO Benchmark Graphs -- 2.3 Related Work -- 3 The Real-World Network -- 4 The Real-World Network Compared to Tailored Benchmark Graphs -- 5 Results -- 5.1 Experiments with the Louvain Method -- 5.2 Experiments with Other Community Detection Algorithms -- 5.3 Comparing the Performance on Tailored Benchmark Graphs to the Performance on the Real-World Network -- 6 Conclusion and Discussion -- References. 327 $aNetwork Based Methodology for Characterizing Interdisciplinary Expertise in Emerging Research -- 1 Introduction -- 2 Related Work -- 3 Expertise-Collaboration Network (ECN) Methodology -- 3.1 Indicators -- 3.2 Expertise-Collaboration Network (ECN) Model -- 3.3 Measures -- 4 Case-Study: IDR Within RETTL Community -- 4.1 Research Context: RETTL Program -- 4.2 Data Collection -- 4.3 Analyzing Available Expertise in the RETTL Community -- 5 Discussions and Conclusions -- References -- Classification Supported by Community-Aware Node Features -- 1 Introduction -- 2 Community-Aware Node Features -- 2.1 Anomaly Score CADA -- 2.2 Normalized Within-Module Degree and Participation Coefficient -- 2.3 Community Association Strength -- 2.4 Distribution-Based Measures -- 3 Experiments -- 3.1 Graphs Used -- 3.2 Node Features Investigated -- 3.3 Experiments -- References -- Signature-Based Community Detection for Time Series -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Asset Graph -- 3.2 Random Matrix Theory -- 3.3 Community Detection -- 3.4 Signature -- 4 Signature-Based Similarity Matrix -- 5 Experimental Evaluation -- 6 Conclusion -- References -- Hierarchical Overlapping Community Detection for Weighted Networks -- 1 Introduction -- 2 Related Work -- 3 Proposed Algorithm for Overlapping Hierarchical Weighted Community Detection -- 3.1 CT-distance in Weighted 2-edge-connected Graph -- 3.2 Community Detection Procedure -- 4 Experiments -- 4.1 Unweighted Synthetic Networks -- 4.2 Sensitivity of Community Detection Methods to Edge Weight -- 4.3 Weighted Synthetic Network -- 5 Conclusion -- References -- Detecting Community Structures in Patients with Peripheral Nervous System Disorders -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 Dataset Description -- 5 Proposed Method -- 5.1 The Projection Phase -- 5.2 Weight Assignment Phase. 327 $a5.3 The Community Detection Phase -- 6 Experiments and Results -- 6.1 Results -- References -- Community Detection in Feature-Rich Networks Using Gradient Descent Approach -- 1 Introduction: Background and Modification -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 Proposed Clustering Methods -- 3 Experimental Setting -- 3.1 Algorithms Under Comparison -- 3.2 Data Sets -- 3.3 Evaluation Criteria -- 4 Scrutinizing the Main Hyperparameters of the Proposed Methods -- 5 Experimental Results -- 5.1 Comparison over Real-Word Data Sets -- 5.2 Comparison over Synthetic Data with Categorical Features -- 6 Conclusion and Future Work -- References -- Detecting Strong Cliques in Co-authorship Networks -- 1 Introduction -- 2 Strong Cliques -- 2.1 Structural Dependency -- 2.2 Dependency Threshold Estimation -- 3 Experiments -- 3.1 Results and Discussion -- 3.2 Dependency Threshold Effect -- 4 Conclusion and Future Work -- References -- Mosaic Benchmark Networks: Modular Link Streams for Testing Dynamic Community Detection Algorithms -- 1 Introduction -- 2 Related Works -- 3 Mathematical Framework -- 3.1 Link Stream -- 3.2 Mosaic: A Definition for a Community in Link Streams -- 3.3 Mosaic Link Stream Benchmark -- 3.4 Scenario Description -- 3.5 Generating Edges -- 4 Experiments -- 5 Discussion and Conclusions -- References -- Entropic Detection of Chromatic Community Structures -- 1 Introduction -- 2 Formalizing the Coloring -- 3 Chromatic Entropy -- 3.1 Chromatic Entropy Definition -- 3.2 Probability of Random Coloring -- 4 Chromatic Community Structure Detection -- 5 Conclusion -- References -- On the Hierarchical Component Structure of the World Air Transport Network -- 1 Introduction -- 2 Data and Method -- 2.1 Data -- 2.2 Methods -- 3 Experimental Results -- 3.1 Component Structure -- 3.2 First Level of Hierarchy -- 3.3 Second Level of Hierarchy. 327 $a4 Discussion -- 5 Conclusion -- References -- Weighted and Unweighted Air Transportation Component Structure: Consistency and Differences -- 1 Introduction -- 2 Mesoscopic Structure Analysis -- 2.1 Community Structure -- 2.2 Component Structure Analysis -- 3 Global Topological Properties of the Components -- 3.1 Clustering Coefficient -- 3.2 Strength Distribution -- 4 Discussion and Conclusion -- References -- Effects of Null Model Choice on Modularity Maximization -- 1 Introduction -- 2 Methods -- 3 Results -- 3.1 General Experimental Set of Networks -- 3.2 Fixed Community Size Distribution Experimental Set -- 4 Discussion -- 4.1 Extension to Explicit Multi-level Methods -- 5 Conclusion -- References -- On Centrality and Core in Weighted and Unweighted Air Transport Component Structures -- 1 Introduction -- 2 Core Structure Analysis -- 2.1 Local Components -- 2.2 Global Component -- 2.3 World Air Transportation Network -- 3 Local Topological Properties -- 3.1 Top Five Nodes Analysis -- 3.2 RBO Analysis -- 4 Discussion and Conclusion -- References -- Diffusion and Epidemics -- New Seeding Strategies for the Influence Maximization Problem -- 1 Introduction -- 2 Related Work -- 2.1 Influence Maximization Problem -- 2.2 Diffusion Models -- 2.3 Seeding Strategies for the IMP -- 3 New Seeding Strategies -- 3.1 CVSP: Connectivity-Based Seeding Strategy -- 3.2 ER: Spectral Seeding Strategy -- 4 Comparison Experiments -- 4.1 Experiment Design, Implementation and Data Sets -- 4.2 Final Influence Spreading Rate Comparison -- 4.3 Visual Analysis and Comparison -- 4.4 Summary and Recommendation -- References -- Effects of Homophily in Epidemic Processes -- 1 Introduction -- 2 Basic Setup -- 2.1 Probability of Epidemics -- 3 Model and Main Results -- 3.1 Multi-type Branching Processes -- 3.2 Generating Functions -- 4 Numerical Studies -- 5 Conclusion. 327 $aA Proof of Theorem 1. 410 0$aStudies in Computational Intelligence Series 700 $aCherifi$b Hocine$01726434 701 $aRocha$b Luis M$01726435 701 $aCherifi$b Chantal$01726436 701 $aDonduran$b Murat$01726437 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910842288303321 996 $aComplex Networks and Their Applications XII$94146933 997 $aUNINA