Complex Networks & Their Applications IX: Volume 1, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020

Preface
Organization and Committees
	General Chairs
	Advisory Board
	Program Chairs
	Satellite Chairs
	Lightning Chairs
	Poster Chairs
	Publicity Chairs
	Tutorial Chairs
	Sponsor Chairs
	Local Committee Chair
	Local Committee
	Publication Chair
	Web Chair
	Program Committee
Contents
Community Structure
A Method for Community Detection in Networks with Mixed Scale Features at Its Nodes
	1 Introduction: Previous Work and Motivation
	2 A Least Squares Criterion
	3 Setting of Experiments for Validation and Comparison of SEFNAC Algorithm
		3.1 Algorithms Under Comparison
		3.2 Datasets
		3.3 Evaluation Criteria
	4 Results of Computational Experiments
		4.1 Parameters of the Generated Datasets
		4.2 Validity of SEFNAC
		4.3 Comparing SEFNAC and Competition
	5 Conclusion
	References
Efficient Community Detection by Exploiting Structural Properties of Real-World User-Item Graphs
	1 Introduction
	2 Related Work
	3 Intuition Behind the Algorithm
	4 Model
	5 Algorithm
	6 Experimental Evaluation
		6.1 Evaluation on Detected Communities
		6.2 Evaluation on Runtime
		6.3 Evaluation on Convergence
	7 Conclusions
	References
Measuring Proximity in Attributed Networks for Community Detection
	1 Introduction
	2 Related Work
	3 Background and Preliminaries
		3.1 Definitions
		3.2 Community Detection Algorithms
		3.3 Measures
		3.4 Clustering Quality Evaluation
	4 Proximity-Based Community Detection in Attributed Networks
	5 Experiments
	6 Results
	7 Conclusion
	References
Core Method for Community Detection
	1 Theory
		1.1 About Revealing Communities and Key Applied Tasks
		1.2 Removing “Garbage” Vertices and Allocating the Core
		1.3 Graphs of Information Interaction
		1.4 Meta-vertices and Meta-graph
		1.5 Core Method
	2 Tool
	3 An Example of Applying the Method on Data from Twitter
		3.1 The Core Detection
		3.2 The Structure of Meta-Vertices
	4 Conclusions
	References
Effects of Community Structure in Social Networks on Speed of Information Diffusion
	1 Introduction
	2 Effects of Community Structure on Diffusion Speed of Tweets
		2.1 Methodology
		2.2 Results
	3 Predicting Diffusion Speed
		3.1 Problem Setting
		3.2 Prediction Method
		3.3 Prediction Results
	4 Conclusion
	References
Closure Coefficient in Complex Directed Networks
	1 Introduction
	2 Preliminaries
		2.1 Clustering Coefficient
		2.2 Closure Coefficient
	3 Closure Coefficient in Directed Networks
		3.1 Closure Coefficient in Binary Directed Networks
		3.2 Closure Coefficients of Particular Patterns
		3.3 Closure Coefficient in Weighted Networks
	4 Experiments and Analysis
		4.1 Directed Closure Coefficient in Real-World Networks
		4.2 Link Prediction in Directed Networks
	5 Conclusion
	References
Nondiagonal Mixture of Dirichlet Network Distributions for Analyzing a Stock Ownership Network
	1 Introduction
	2 Related Works
		2.1 Sparse Block Model
		2.2 Mixture of Dirichlet Network Distributions
	3 Nondiagonal Mixture of Dirichlet Network Distributions
		3.1 Generating Process
		3.2 Inference
	4 Results
		4.1 Dataset
		4.2 Quantitative Comparison
		4.3 Estimated Block Structure
	5 Conclusion
	References
Spectral Clustering for Directed Networks
	1 Introduction
		1.1 Motivation
		1.2 General Spectral Clustering Algorithm
	2 Spectral Clustering for Directed Graphs
	3 Simulation Study
	4 Congressional Cosponsorship
	5 Conclusion
	References
Composite Modularity and Parameter Tuning in the Weight-Based Fusion Model for Community Detection in Node-Attributed Social Networks
	1 Introduction
	2 WBFM Within ASN CD Problem and Its Logical Gap
		2.1 Description of WBFM and Related ASN CD Problem
		2.2 WBFM CD Quality Evaluation Process and Its Logical Gap
	3 Related Works
	4 Theoretical Study
	5 Parameter Tuning Scheme
	6 Experiments
		6.1 Synthetic Node-Attributed Networks
		6.2 Real-World Node-Attributed Networks
		6.3 Evaluation of the Proposed Parameter Tuning Scheme and Attributes-Aware Modularity
	7 Conclusions
	References
Maximal Labeled-Cliques for Structural-Functional Communities
	1 Introduction
	2 Background: Maximal-Labeled Cliques
	3 Community Detection
		3.1 Null Model for Labeled-Graphs
		3.2 Structural-Functional Divergence
		3.3 Structural-Functional Clustering
		3.4 Quality of Structural-Functional Clustering
	4 Evaluation Results
		4.1 SF-Divergence
		4.2 Discovering Overlapping Communities
	5 Conclusion
	References
Community Detection in a Multi-layer Network Over Social Media
	1 Introduction
	2 Related Work
		2.1 Community Detection Methods in a Multilayer Network
	3 Proposed Work
		3.1 Dataset
		3.2 Proposed Approach
		3.3 Network Formulation
	4 Results
		4.1 Community Detection in Multilayer Network
		4.2 Social Network Analysis of Merged User Graph
		4.3 Temporal Analysis of User’s Polarity in Network
	5 Conclusion
	References
Using Preference Intensity for Detecting Network Communities
	1 Introduction
	2 A New Approach for Community Detection
	3 Preference Relations
	4 Preference Relation Properties
	5 Framework of the Preference-Based Method
	6 Experiments and Results
	7 Conclusions and Future Work
	References
Community Detection Algorithm Using Hypergraph Modularity
	1 Motivation and Our Contribution
	2 Modularity Functions
		2.1 Modularity Function for Graphs
		2.2 Using Graph Modularity for Hypergraphs
		2.3 Modularity Function for Hypergraphs
		2.4 Unification and Generalization
	3 Algorithms
		3.1 Louvain—Graph-Based Algorithm
		3.2 Kumar et al.—Refinement of Graph-Based Algorithm
		3.3 LS and HA—Our Prototypes
	4 Synthetic Random Hypergraph Model
	5 Experiments
	6 Conclusions and Future Directions
	References
Towards Causal Explanations of Community Detection in Networks
	1 Introduction
	2 The Causal Model for Community Detection
		2.1 Preliminaries
		2.2 The Proposed Approach
	3 Algorithmic Aspects
		3.1 The General Framework
		3.2 Working with Modularity-Based Methods
	4 Additional Issues and Extensions
	References
A Pledged Community? Using Community Detection to Analyze Autocratic Cooperation in UN Co-sponsorship Networks
	1 Motivation
	2 Autocratic Cooperation – What We Know and What We Do Not Know
		2.1 Scientific Background and Theoretical Argument
		2.2 The Missing Piece of the Puzzle
	3 Our Approach: Co-sponsorship Networks of UNGA Resolutions
	4 Method
	5 Results
	6 Discussion
	References
Distances on a Graph
	1 Introduction
	2 Distance, Intra-cluster Density and Graph Clustering (Network Community Detection)
	3 Distance Measurements Under Study
		3.1 Embedding, Commute and Amplified Commute Distances
		3.2 Jaccard Distance
		3.3 Otsuka-Ochiai Distance
		3.4 Burt's Distance
	4 Numerical Comparisons
		4.1 Test Data: Synthetic Graphs with Known Clusters
		4.2 Empirical Results
		4.3 Noise, Sensitivity and Convergence
	5 Our Chosen Distance
	6 Metric Space and the Jaccard Distance
	7 Conclusion
	References
Local Community Detection Algorithm with Self-defining Source Nodes
	1 Introduction
	2 Related Work
	3 Preliminaries and Notation
	4 Self-defining Local Community Detection
	5 Experimental Analysis
		5.1 Evaluating Quality of Communities
		5.2 Source Node Selection Analysis
		5.3 Computational Complexity Analysis
	6 Conclusion and Future Work
	References
Investigating Centrality Measures in Social Networks with Community Structure
	1 Introduction
	2 Preliminaries and Definitions
		2.1 Classical Centrality Measures
		2.2 Community-Aware Centrality Measures
	3 Datasets and Materials
		3.1 Data
		3.2 Tools
	4 Experimental Results
		4.1 Correlation Analysis
		4.2 Similarity Analysis
	5 Conclusion
	References
Network Analysis
Complex Network Analysis of North American Institutions of Higher Education on Twitter
	1 Introduction
	2 Data Set
	3 Network Construction
		3.1 A Note on Edge Weight Calculations
	4 Network Analysis
		4.1 Monadic Analysis
		4.2 Dyadic Analysis
		4.3 Community Analysis
	5 Followers' Analysis
	6 Discussion
	7 Conclusion
	References
Connectivity-Based Spectral Sampling for Big Complex Network Visualization
	1 Introduction
	2 Related Work
		2.1 Graph Sampling and Spectral Sparsification
		2.2 BC (Block Cut-Vertex) Tree Decomposition
		2.3 Graph Sampling Quality Metrics
	3 BC Tree-Based Spectral Graph Sampling
		3.1 Algorithm BC_SS
		3.2 Algorithm BC_SV
	4 BC_SS and BC_SV Experiments
		4.1 Runtime Improvement
		4.2 Approximation on the Effective Resistance Values
		4.3 Approximation on the Ranking of Edges and Vertices
		4.4 Graph Sampling Quality Metrics Comparison
		4.5 Jaccard Similarity Index Comparison
		4.6 Visual Comparison: SS vs. BC_SS and SV vs. BC_SV
	5 Conclusion and Future Work
	References
Graph Signal Processing on Complex Networks for Structural Health Monitoring
	1 Introduction
	2 Background on GSP
	3 Method
		3.1 Overview: GSP Methodological Framework
		3.2 Dataset
		3.3 Network Creation
		3.4 Node Subset Selection – Sensor Subset Sampling
	4 Results and Discussion
		4.1 Sampling: Selecting a Minimal Subset of Sensors
		4.2 Network Representation Example: Girders and Deck
		4.3 Identification of Mode Shapes
	5 Conclusions
	References
An Analysis of Four Academic Department Collaboration Networks with Respect to Gender
	1 Introduction
	2 Related Works
	3 Methods
	4 Properties of the Collaboration Network
	5 Claims
		5.1 Claim: Men Tend to Have More Collaborators
		5.2 Claim: Women Tend to Repeatedly Collaborate with the Same Collaborators
		5.3 Claim: Researchers Tend to Collaborate with Authors of the Same Gender
		5.4 Claim: Women Tend to Collaborate More Intramurally
	6 Conclusions and Future Work
	References
Uncovering the Image Structure of Japanese TV Commercials Through a Co-occurrence Network Representation
	1 Introduction
	2 Methods
	3 Results
		3.1 Degree and Strength
		3.2 Community Structure
	4 Discussion
	References
Movie Script Similarity Using Multilayer Network Portrait Divergence
	1 Introduction
	2 Background
		2.1 Extracting Multilayer Networks from Movie Scripts
		2.2 Network Comparison Using Portrait and Portrait Divergence
	3 Experimental Evaluation
		3.1 Comparing Portraits
		3.2 Comparing Portrait Divergence
	4 Discussion and Conclusion
	References
Interaction of Structure and Information on Tor
	1 Introduction
	2 Related Work
	3 Structural Identity of Tor Domains
		3.1 Representation of Tor Structural Identity
		3.2 Clustering Tor Structural Identity
	4 Conclusion and Future Work
	References
Classifying Sleeping Beauties and Princes Using Citation Rarity*-6pt
	1 Introduction
	2 Results
		2.1 Sleeping Beauties and Princes
		2.2 Defining the SB–PR Pair Density
		2.3 Density Distribution
		2.4 Rediscovering PRs and Exploring PRs
		2.5 Relation Type of SBs and Princes
		2.6 Density vs. Citation
	3 Conclusion
	4 Data
	References
Finding High-Degree Vertices with Inclusive Random Sampling
	1 Introduction
		1.1 Random Neighbor
		1.2 Random Edge
		1.3 Inclusive Sampling
	2 Sampling Method Comparisons
		2.1 Calculating the Expectations
		2.2 Strengths of RN and RE
		2.3 RE/RN and RN/RE Are Both Unbounded
		2.4 RE and RN in Trees
	3 Sampling Methods in Synthetic and Real-World Graphs
		3.1 Synthetic Graphs
		3.2 Real-World Networks
	4 Inclusive Sampling Methods and Degree Homophily
	5 Summary and Future Research Directions
	References
Concept-Centered Comparison of Semantic Networks
	1 Introduction
	2 Semantic Networks and Concept-Centered Networks
	3 Data Description
	4 The Choice of the Threshold Value
	5 Illustration
	6 Concluding Remarks
	References
Diffusion and Epidemics
Analyzing the Impact of Geo-Spatial Organization of Real-World Communities on Epidemic Spreading Dynamics
	1 Introduction
	2 The Geo-Spatial Population Model
		2.1 Real Geo-Spatial Data
		2.2 Epidemic Reference Data
	3 Results
	4 Conclusions
	References
Identifying Biomarkers for Important Nodes in Networks of Sexual and Drug Activity
	1 Introduction
	2 Related Work
	3 Methodology
		3.1 Data Acquisition and Curation
		3.2 Calculating Betweenness Centrality
		3.3 Correlation of Features with High Betweenness
	4 Results
		4.1 Scale-Free Underlying Networks
		4.2 City Graphs with High Betweenness Nodes
		4.3 Exceptional Attributes per City
		4.4 Unique Attributes of High Betweenness Nodes
	5 Discussion and Conclusion
	References
Opinion Dynamic Modeling of Fake News Perception
	1 Introduction
	2 Related Works
	3 Fake News: Opinion Dynamic Modeling
	4 Experimental Analysis
	5 Conclusion
	References
Influence Maximization for Dynamic Allocation in Voter Dynamics
	1 Introduction
	2 Model Description
	3 Results
		3.1 Mean-Field Analysis
		3.2 Optimal Strategies for Controller A
	4 Conclusion
	References
Effect of Interaction Mechanisms on Facebook Dynamics Using a Common Knowledge Model
	1 Introduction
		1.1 Background and Motivation
		1.2 Contributions of This Work
	2 Related Work
	3 Model
		3.1 Preliminaries
		3.2 Facebook Common Knowledge Model Mechanisms
	4 Social Networks
	5 Agent-Based Model and Simulation Parameters
	6 Simulation Results
	7 Conclusion
	References
Using Link Clustering to Detect Influential Spreaders
	1 Introduction
	2 Background
		2.1 Properties of the Network
	3 Approach
		3.1 Link Communities
	4 Evaluation
		4.1 Metrics for Evaluation
		4.2 Results
	5 Discussion
	References
Prediction of the Effects of Epidemic Spreading with Graph Neural Networks
	1 Introduction
	2 Related Work
		2.1 Analysis of Spreading Processes
		2.2 Machine Learning on Networks
	3 Task Formulation
	4 Proposed Methodology
	5 Empirical Evaluation
		5.1 Baselines
		5.2 Experimental Setting
		5.3 Results
		5.4 Interpretation of a Prediction
	6 Discussion and Conclusions
	References
Learning Vaccine Allocation from Simulations
	1 Introduction
	2 Related Work
	3 Problem Statement
		3.1 Continuous-Time Networked SIR Model
		3.2 Vaccination Allocation Problem
	4 Our Method
		4.1 Rejection-Based Simulation
		4.2 Impact Score Estimation
		4.3 Introducing Simba
		4.4 Discussion
		4.5 Generalizations
	5 Experimental Results
	6 Conclusions and Future Work
	References
Suppressing Epidemic Spreading via Contact Blocking in Temporal Networks
	1 Introduction
	2 Methods
		2.1 Link Centrality Metrics
		2.2 Contact Removal Probability
		2.3 Datasets
		2.4 Simulation
	3 Results
	4 Conclusion and Discussion
	References
Blocking the Propagation of Two Simultaneous Contagions over Networks
	1 Introduction
	2 Definitions and Analytical Results
	3 Experimental Results
	4 Future Research Directions
	References
Stimulation Index of Cascading Transmission in Information Diffusion over Social Networks
	1 Introduction
	2 Related Work
		2.1 Information Diffusion Model
		2.2 Analysis and Estimation of Information Diffusion
	3 Stimulation Index of Cascading Transmission
		3.1 Basic Idea of Proposed Method
		3.2 Calculation Method
	4 Experimental Settings
		4.1 Generation of Follow Network
		4.2 Simulation of Information Diffusion
	5 Experimental Evaluations
		5.1 Does Removing the Edge with a High Stimulation Index Inhibit Information Diffusion?
		5.2 Is There a Correlation Between the Stimulation Index and the Number of Activations and Activated Nodes?
	6 Discussion
	7 Conclusion
	References
Diffusion Dynamics Prediction on Networks Using Sub-graph Motif Distribution
	1 Introduction
	2 Literature
		2.1 Motifs and Dynamics on Networks
		2.2 Motif Detection Methods Implementations
	3 Network Data
	4 Method
		4.1 Motif Detection Methods
		4.2 Subgraph Sampling for Representation of Motifs
		4.3 Motifs and Process Spreading: Regression Task Statement
		4.4 Self-similarity and Dynamics on Networks
	5 Results
		5.1 Dynamics on Networks and Motifs
		5.2 Sampling Techniques
		5.3 Self-similarity and Dynamics
	6 Discussion and Conclusion
	References
Using Distributed Risk Maps by Consensus as a Complement to Contact Tracing Apps
	1 Introduction
	2 Collaborative Risk Map Generation
		2.1 Extension of the Consensus Process
		2.2 Map Generation
	3 Results
		3.1 Population and Infection Model
		3.2 Risk Map Creation
		3.3 Evolution with Contact Tracing App Active
		3.4 Contact Tracing and Risk Maps Combined
	4 Conclusions
	References
Dynamics on/of Networks
Distributed Algorithm for Link Removal in Directed Networks
	1 Introduction
	2 Problem Statement
		2.1 Notation and Premilinaries
		2.2 Problem Formulation
	3 Main Result
		3.1 Distributed Estimation of Dominant Right Eigenvector w0
		3.2 Distributed Estimation of Dominant Left Eigenvector 0
		3.3 Distributed Verification of Digraph's Strong Connectivity
		3.4 The Complete Distributed Link Removal Algorithm
	4 An Illustrative Example
	5 Conclusion
	References
Data Compression to Choose a Proper Dynamic Network Representation
	1 Introduction
	2 Context and Motivation
		2.1 The Different Models of Dynamic Networks
		2.2 Using Encoding Cost as a Selection Criterion
		2.3 Applications
	3 Temporal Network Encoding Cost
	4 Experiments
		4.1 Synthetic Networks
		4.2 Experiments with Real Networks
	5 Conclusion
	References
Effect of Nonisochronicity on the Chimera States in Coupled Nonlinear Oscillators
	1 Introduction
	2 Swing-By Mechanism and Chimera Death in Coupled Stuart-Landau Oscillators Under Nonlocal Coupling with Symmetry Breaking
		2.1 Characterization of Chimera and Other Collective States
		2.2 Collective States in the (,c) Parameter Space
	3 Conclusion
	References
Evolution of Similar Configurations in Graph Dynamical Systems
	1 Introduction
	2 Preliminaries
	3 Analytical Results
	4 Experimental Results
	5 Summary and Future Research Directions
	References
Congestion Due to Random Walk Routing
	1 Introduction
	2 General Results
		2.1 Time Evolution Equations
		2.2 Steady State Solution
		2.3 Discussion
	3 Numerical Results
	4 Conclusions
	References
Strongly Connected Components in Stream Graphs: Computation and Experimentations
	1 The Stream Graph Framework
	2 Strongly Connected Components
		2.1 Direct Approach
		2.2 Fully Dynamic Approach
	3 Experiments and Applications
		3.1 Datasets
		3.2 Algorithm Performances
		3.3 Connectedness Analysis of IP Traffic
		3.4 Approximate Strongly Connected Components
		3.5 Application to Latency Approximation
	4 Related Work
	5 Conclusion
	References
The Effect of Cryptocurrency Price on a Blockchain-Based Social Network
	1 Introduction
	2 Steemit: A Blockchain-Based Online Social Network
	3 Dataset
	4 Methods
	5 Results
	6 Conclusions
	References
Multivariate Information in Random Boolean Networks
	1 Introduction
	2 Preliminaries
	3 O-Information in Random Boolean Networks
	4 Phase Diagram Anatomy
		4.1 Ordered Regime
		4.2 Critical Point
	5 Conclusions
	References
Earth Sciences Applications
Complexity of the Vegetation-Climate System Through Data Analysis
	1 Introduction
	2 Material and Methodology
		2.1 Study Case and Plot Selection
		2.2 Acquisition of Satellite Data and MSAVI Calculation
		2.3 Meteorological Variables
		2.4 Date-to-Date Analysis
		2.5 Cross-Correlations by Phase
		2.6 Recurrence Plots and Recurrence Quantification Analysis
	3 Results and Discussion
		3.1 Box Plots and Phases Analysis
		3.2 Cross-Correlation by Phase
		3.3 Differencing Vegetation Index Series and Parameter Optimization
		3.4 Recurrence Quantification Analysis
	4 Conclusions
	References
Towards Understanding Complex Interactions of Normalized Difference Vegetation Index Measurements Network and Precipitation Gauges of Cereal Growth System
	1 Introduction
	2 Methods
		2.1 Case Study and Data
	3 Results
		3.1 Analytical Advances of NDVI Cereal Time Series
		3.2 Scaling Characteristics of Precipitation Series
	4 Conclusions
	References
Spatio-Temporal Clustering of Earthquakes Based on Average Magnitudes
	1 Introduction
	2 Related Work
		2.1 Declustering Algorithms
		2.2 Change-Point Detection Algorithms
	3 Proposed Method
		3.1 Tree Construction Strategies
		3.2 Tree Separation Algorithm
	4 Experimental Evaluation
		4.1 Quantitative Evaluation
		4.2 Visual Evaluation
	5 Conclusion
	References
Information Spreading in Social Media
Analyzing the Robustness of a Comprehensive Trust-Based Model for Online Social Networks Against Privacy Attacks
	1 Introduction
	2 Background and Related Work
	3 The Comprehensive Trust-Based Model
	4 Analysis of Attack Scenarios on the Model
		4.1 Attack Definitions and Scenario
		4.2 Optimizing the Attack: Minimizing the Connections of Fake Users by Reduction from Minimum Vertex Cover
	5 Evaluation
	6 Discussion, Conclusion, and Future Work
	References
Media Partisanship During Election: Indonesian Cases
	1 Introduction
	2 Data
	3 Method for Political Stance Detection of the Online News Outlets
		3.1 Hashtag-Based User Labeling
		3.2 Network-Based User Labeling
		3.3 Media Classification
	4 Analysis
		4.1 The Political Stance of News Media Outlets
	5 Conclusion
	References
Media Polarization on Twitter During 2019 Indonesian Election
	1 Introduction
	2 Data
	3 Method
		3.1 Bipartite Network
		3.2 Community Structure
		3.3 Political Stance of Online News Media
	4 Analysis
		4.1 News Consumption Pattern
		4.2 Segregation in Media Network
		4.3 Political Polarization
		4.4 Interaction Across Political Communities
		4.5 News Media Centrality
	5 Conclusion
	References
Influence of Retweeting on the Behaviors of Social Networking Service Users
	1 Introduction
	2 Related Works
	3 Proposed Model
		3.1 Reward Game with Retweeting
		3.2 Evolutionary Process in Networked Agents
	4 Experiment
		4.1 Experimental Settings
		4.2 Experimental Results - Complete Graph
		4.3 Experimental Results - CNN Networks
		4.4 Discussion
	5 Conclusion
	References
Author Index