Complex Networks & Their Applications XII

Name: Complex Networks & Their Applications XII | Proceedings of The Twelfth International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023, Volume 2
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Proceedings of The Twelfth International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023, Volume 2

Hocine Cherifi Luis M. Rocha Chantal Cherifi Murat Donduran(Editor)

Springer (Publisher)

Published on 28. February 2024

XXII, 504 pages

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978-3-031-53499-7 (ISBN)

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Hocine Cherifi is a professor of Computer Science at the University of Burgundy, Dijon, France, since 1999. He completed his Ph.D. degree at the National Polytechnic Institute, Grenoble, in 1984. Before moving to Dijon, he held faculty positions at Rouen University and Jean Monnet University, in France. He has held visiting positions at Yonsei, Korea, University of Western Australia, Australia, National Pingtung University, Taiwan; and Galatasaray University, Turkey. His recent research interests are in computer vision and complex networks. He has published more than 200 scientific papers in international refereed journals and conference proceedings. He held leading positions in more than 15 international conference organizations (General chair, Program chair), and he served on more than 100 program committees. He is the founder of the International Conference on Complex Networks \& their Applications. Currently, he is also a member of the Editorial Board of Computational Social Networks, PLOS One, IEEE Access, Journal of Imaging, Complex Systems, Quality and Quantity, and Scientific Reports. He is the Founding Editor-in-Chief of the Applied Network Science journal.
Luis M. Rocha is the George J. Klir Professor of Systems Science at the Thomas J. Watson College of Engineering and Applied Science, Binghamton University, where he leads the Complex Adaptive Systems and Computational Intelligence (CASCI) lab. He is also Senior Fellow at the Instituto Gulbenkian da Ciencia, co-director of the Consortium for Social and Biomedical Complexity between Binghamton University and Indiana University, and a founding partner of the International Center for Excellence in Mental Health Sciences. He has been the director of the NSF-NRT Interdisciplinary Training Program in Complex Networks and Systems, and Professor of Informatics at the Luddy School of Informatics, Computing, and Engineering at Indiana University. His research is on complex networks and systems,computational and systems biology, and computational intelligence. He received his PhD in Systems Science in 1997 from the State University of New York at Binghamton. From 1998 to 2004 he was a permanent staff scientist at the Los Alamos National Laboratory, where he founded and led a Complex Systems Modeling Team during 1998-2002, and was part of the Santa Fe Institute research community. He has organized major conferences in the field such as Alife X, ECAL 2007, and Complex Networks 2019-2023. He has published many articles in scientific and technology journals and has been the recipient of several scholarships and awards.
Chantal Cherifi received her PhD in Computer science from Corsica University, France, in 2011. Since 2014, she is working as an Associate Professor at the DISP laboratory, at the University of Lyon 2, France. Her main research interests are focused on information systems agility and big data management with applications on enterprise information systems and smart cities, using complex networks, ontologies, and Product Lifecycle Management (PLM) systems tools. She is involved in several international conference organizations such as Complex Networks, CompleNet, PLM, and DICTAP. She serves as a member of international conferences program committees of Complex Networks, CompletNet, Complexis, ISCRAM-med, CSCESM, SITIS, and ICIEIS; and journal referee for IJCIM, EPL, Scientific Reports, and Nature. She is a member of EU Erasmus-Mundus programs (SmartLink and cLink program). Her local responsibilities include Committee Lab member, since 2016, and Lab seminar co-organizer, since 2014.
Murat Donduran is a Professor of Economics at the Department of Economics, YILDIZ Technical University Istanbul Türkiye, where he is the Director of the Graduate School of Social Sciences. He is also the Board member of in Turkish Economic Foundation. His research is on microeconomics, computational economics, firm dynamics, and game theory. He received his Bachelor's and Master's Degrees in Economics from Marmara University, Istanbul, Türkiye, and a Ph.D. in Economics in 2000 from the YILDIZ Technical University. Since 2000 he has been a faculty member in the Faculty of Economic and Administrative Sciences at the YILDIZ Technical University. He has organized several conferences in the field such as the YILDIZ International Conference on Social Sciences, the Annual International Conference on Social Sciences (2016-2020), and the International Conference on Economics (IceTea) 2019-2023. He has published many articles in scientific journals such as Games and Economic Behavior, Review of Industrial Organization, Physica A, and North American Journal of Economics and Finance.

Content

Intro
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
2.2 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
Network 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
5.3 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
4 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
A Proof of Theorem 1
References
Human Papillomavirus Co-circulation on a Partially Vaccinated Partnership Network
1 Introduction
2 Related Works
3 Methods
3.1 Overview of the Model
3.2 Design Concept
4 Results
4.1 Data
4.2 Input and Simulations
4.3 Output
5 Discussion
Appendix
References
Towards the Building of a Surveillance Network for PPR-Like Diseases in Nigeria: Identifying Potential Sentinel Node in a Partially-Known Network
1 Introduction
2 Material and Methods
2.1 Data and Epidemic Simulation
3 Seeds' Cluster Detection
4 Node's Vulnerability and Definition of Sentinel Nodes
5 Results
6 Discussion
References
Travel Demand Models for Micro-Level Contact Network Modeling
1 Introduction
2 Background
3 Methodology
3.1 Temporal-Dynamic Contact Networks
3.2 Mobility Data
3.3 Micro-Level Contact Modeling
3.4 Unveiling Topological Properties with SIR Model
4 Results
4.1 SIR-Based Evaluation
4.2 Effect of Hyperparameter Settings
5 Conclusion
References
Evaluating Attitudes on Health-Seeking Behavior Among a Network of People Who Inject Drugs
1 Introduction
2 Methods
2.1 Causal Inference Framework under the Presence of Dissemination
3 Results
4 Discussion
References
On the Relation Between Replicator Evolutionary Dynamics and Diffusive Models on General Networks
1 Introduction
2 The Model
3 Replicator Equation Model Development
3.1 Related Model
3.2 Combining the SI Model and Replicator Equation
3.3 The Network Structure of Information Diffusion
3.4 Network Structure on Game
4 Results
5 Conclusion
References
Dynamics on/of Networks
SMART CONTRACTS Based Peer to Peer Communication in Blockchain: A Decentralized Approach
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Validation using Smart Contracts
3.2 Proof-of-Work (PoW)
3.3 Cluster-Authority Selection (CAS)
3.4 Blockchain Registration
3.5 Smart Contract-Based Verification
3.6 Send/Receive Message
4 Advantages and Limitations
5 Conclusion and Future Scope
References
A Quadratic Static Game Model for Assessing the Impact of Climate Change
1 Introduction
2 Simple Climate Model (SCM)
2.1 Carbon Cycle Model
2.2 Radiative Forcing
2.3 Temperature Dynamic Model
3 Game-Theoretic Analysis
3.1 Carbon Emission Game Model
3.2 Existence and Uniqueness of a Pure Nash Equilibrium
3.3 Expression of the Nash Equilibrium
4 Numerical Analysis
5 Conclusion
References
Linear Stochastic Processes on Networks and Low Rank Graph Limits
1 Introduction
1.1 Motivation: Networked Systems and Graphons
2 Preliminaries
2.1 Notation
2.2 Q-Noise
2.3 Linear Dynamical Systems
2.4 Finite Network Systems
3 Numerical Examples
3.1 Low Rank Graphon Systems
3.2 Stochastic Block Matrices
4 Future Directions
References
Uniform Generation of Temporal Graphs with Given Degrees
1 Introduction
1.1 Related Work
1.2 Our Contribution
1.3 Overview of Techniques
2 Temporal Configuration Model
3 Algorithm T-Gen
3.1 Initial Conditions
3.2 Stage 1: Removal of Temporal Single-Loops
3.3 Stage 2: Removal of Temporal Double-Edges
References
A Multi-order Adaptive Network Model for Pathways of DNA Methylation and Its Effects in Individuals Developing Post-traumatic Stress Disorder
1 Introduction
2 Background Information on DNA Methylation and Its Effects on Individuals with Post-traumatic Stress Disorder
3 Methods
4 The Introduced Integrative Adaptive Network Model
5 Simulation Results
5.1 Scenario 1: Development of Post-Traumatic Stress Disorder
5.2 Scenario 2: Development of Post-Traumatic Stress Disorder (only NR3C1)
5.3 Scenario 3: Development of Post-Traumatic Stress Disorder (only FKPB5)
5.4 Scenario 4: Symptom Reduction by Administration of MDMA
6 Discussion
References
DynamicScore: A Novel Metric for Quantifying Graph Dynamics
1 DynamicScore
2 Analysis of the Dynamics of the Preferential Attachment Growing Model
2.1 Introduction to the Model
2.2 DynamicScore
3 Generator of Edge-Markovian Graphs
3.1 The Model
3.2 Known Properties of EMGG
3.3 EMGG and E-DynamicScore
3.4 Relationship with the DynamicScore
4 Conclusion and Open Problems
References
A Novel Method for Vertex Clustering in Dynamic Networks
1 Introduction
2 Related Work
3 Spatiotemporal Graph k -means (STGkM)
4 Results
4.1 Algorithmic Analysis
4.2 Connected Components
4.3 Experimental Insights
5 Conclusion
References
A Particle Method for Continuous Hegselmann-Krause Opinion Dynamics
1 Introduction
2 A Time-Continuous Model
2.1 Opinion Space and Opinion Holder Distributions
2.2 Particle Representation of Opinion Distributions
2.3 The Dynamics of Conformist Opinion Holders
2.4 Examples
2.5 Concentration of the Opinion Holder Density
3 A Space-Time-Continuous Model
3.1 Differential-Integral Formulation
3.2 Differential Formulation
3.3 Concentration of the Locally Averaged Opinion Holder Density
4 Numerical Convergence Tests
5 Summary and Discussion
References
Optimal Reconstruction of Graph Evolution Dynamics for Duplication-Based Models
1 Introduction
2 Problem Formulation
3 ILP-DMR: Integer Linear Programming-Based Solution
3.1 Our ILP
3.2 Anchors, Duplicated Nodes and Neighbours
3.3 Phantom Edges
3.4 Edge Reconstruction
4 Results
4.1 Synthetic Data
4.2 Real Networks
5 Conclusions
References
Farthest-First Traversal for Identifying Multiple Influential Spreaders
1 Introduction
2 Method
3 Results
4 Conclusions
References
Wishful Thinking About Consciousness
1 Introduction
1.1 Quantum Consciousness (QC)
1.2 Integrated Information Theory (IIT)
1.3 Very Large Scale (VLS) Dynamical System Simulations (DSS)
2 Comparisons
References
Author Index

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Complex Networks & Their Applications XII

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