Complex Networks and Their Applications XI: Proceedings of The Eleventh International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2022—Volume 2

Organization and Committees
Preface
Contents
Network Models
Modularity of the ABCD Random Graph Model with Community Structure
	1 Introduction
		1.1 Summary of Results
		1.2 Simulations
		1.3 Open Problems
	2 Definitions (of ABCD Model and Modularity)
		2.1 Asymptotic Notation
		2.2 ABCD Model
		2.3 Modularity Function
	3 Related Results for Random Graphs
	4 Some Properties of ABCD
		4.1 Degree Distribution
		4.2 Distribution of Community Sizes
		4.3 Assigning Nodes into Communities and Distribution  of Weights
	5 Modularity
		5.1 Modularity of the Ground-Truth Partition: q(C)
		5.2 Maximum Modularity: q*(G)
	References
Learning Attribute Distributions Through Random Walks
	1 Introduction
	2 Attribute Network Models with Homophily
	3 Network Sampling Schemes
	4 Statistical Learning Methods
		4.1 Discrete Attributes
		4.2 Continuous Attributes
	5 Numerical Studies
		5.1 Synthetic Networks
		5.2 Real Networks
	6 Discussion and Future Directions
	References
A More Powerful Heuristic for Balancing an Unbalanced Graph
	1 The Problem of Balancing an Unbalanced Graph
	2 Preliminaries
		2.1 Verifying Balancedness of G Via Node Labels s(x)
		2.2 An Efficient Version of the Algorithm in ch3alabandi2021discovering Based on Eq. (1)
	3 Flipping Edges in T to Balance G
		3.1 Selection Criteria for a Tree Edge (u, v) for Flipping
		3.2 The New More Powerful Heuristic Algorithm
	4 An Efficient Implementation of MinimalFlipSet
		4.1 Algorithm to Determine the Counts in Tables1 and 2
		4.2 Selecting a Best Flipping Tree-Edge in a Depth-First Tree
	5 Conclusion
	References
DC-RST: A Parallel Algorithm for Random Spanning Trees in Network Analytics
	1 Introduction and Background
	2 Parallel Algorithm and Analysis
		2.1 Algorithm Outline
		2.2 Theoretical Analysis
		2.3 Statistical Analysis
	3 Performance Analysis
		3.1 Implementation Details
		3.2 Performance on a Symmetric Multiprocessor Architecture
	4 Conclusion
	References
A Stochastic Approach for Extracting Community-Based Backbones
	1 Introduction
	2 Acquaintance-Overlapping Backbone (AOB)
	3 Datasets
	4 Evaluation Measures
	5 Experimental Results
	6 Conclusion
	References
Correcting Output Degree Sequences in Chung-Lu Random Graph Generation
	1 Introduction
	2 Methods
		2.1 Greedy Updates
		2.2 Maximum Likelihood Estimation
	3 Results
	4 Discussion
		4.1 Parameters
		4.2 Timing Considerations
	5 Conclusion
	References
Switching In and Out of Sync: A Controlled Adaptive Network Model of Transition Dynamics in the Effects of Interpersonal Synchrony on Affiliation
	1 Introduction
	2 Background Literature
	3 Self-Modelling Network Modelling
	4 The Adaptive Network Model
		4.1 Base Level
		4.2 Modelling Controlled Adaptation
	5 Simulation Results
		5.1 Comparing Time Intervals with Transitions and Time Intervals Without Transitions
		5.2 Comparing Continuous Synchrony Without Transitions with Synchrony with Transitions
	6 Discussion
	References
Uniformly Scattering Neighboring Nodes of an Ego-Centric Network on a Spherical Surface for Better Network Visualization
	1 Introduction
	2 Notations and Definitions
	3 Method
		3.1 Preliminaries
		3.2 Three-Stage Optimization
		3.3 A Connection to Minimum Energy Designs
		3.4 A Demonstration
	4 Performance Comparison
	5 Conclusion
	References
The Hyperbolic Geometric Block Model and Networks with Latent and Explicit Geometries
	1 Introduction and Background
		1.1 Related Work
	2 Hyperbolic Geometric Block Model
	3 Combining Latent and Explicit Geometries
	4 Discussion and Conclusions
	References
A Biased Random Walk Scale-Free Network Growth Model with Tunable Clustering
	1 Introduction
	2 Literature Review
	3 Biased Random Walk Model
		3.1 Random Walk (RW)
		3.2 Biased Random Walk (BRW)
		3.3 Algorithm of the Biased Random Walk Model
	4 Experimental Results
		4.1 Biased Random Walk
		4.2 Degree Distribution Analysis
		4.3 Clustering Coefficient Analysis
	5 Conclusions
	References
The Distance Backbone of Directed Networks
	1 Introduction
	2 Closures in Complex Networks
		2.1 Transitive Closure
		2.2 Distance Closure
		2.3 Shortest-Path, Metric and Ultrametric Closures
		2.4 Distance Backbone Subgraph
	3 Directed Distance Backbone
		3.1 Redundancy and Robustness
	4 Experimental Analysis
		4.1 Giraffe Socialization
		4.2 London Bike-Sharing Trips
		4.3 U.S. Airport Transportation
	5 Discussion
	6 Conclusion
	References
Community Structure
Structure of Core-Periphery Communities
	1 Introduction
	2 Related Work
	3 Core-Periphery Community
	4 Utility of Periphery Agents
	5 Agents' Decisions and Interactions
		5.1 Core Agent's Decision Problem
		5.2 Periphery Agents' Decision Problem
		5.3 Nash Equilibrium
		5.4 Existence of Unique Nash Equilibrium
	6 Structural Properties of Core-Periphery Communities
		6.1 Condition for Core-Periphery Communities to Emerge
		6.2 Connectivity Between Periphery Agents
		6.3 Following Rates
	7 Conclusions
	References
Outliers in the ABCD Random Graph Model with Community Structure (ABCD+o)
	1 Introduction
	2 Adjusting the ABCD Model to Include Outliers
		2.1 The Original Model
		2.2 Adjusting the Model to Include Outliers
	3 Experiments—Distinguishing Properties of Outliers
		3.1 The College Football Graph
		3.2 Participation Coefficient
		3.3 ECG Votes
	4 Future Directions
	References
Influence-Based Community Deception
	1 Introduction
	2 Background
	3 Deception in Directed Influence Networks (DIN)
		3.1 Effect of Edge Modification on the Modularity Loss
		3.2 The InflDec Deceptor
	4 Experimental Evaluation
		4.1 Deception Score Comparison
	5 Concluding Remarks and Future Work
	References
AutoGF: Runtime Graph Filter Tuning for Community Node Ranking
	1 Introduction
	2 Background
	3 Tuning Graph Filters at Runtime
	4 Experiment Setup
	5 Experiment Results
	6 Conclusions and Future Work
	References
Dynamic Local Community Detection with Anchors
	1 Introduction
		1.1 Contributions
	2 Related Work
	3 Dynamic Local Community Detection with Anchors
		3.1 Preliminaries and Problem Formulation
		3.2 Proposed Framework
	4 Experiment Design
		4.1 Datasets
		4.2 Evaluation Metrics
		4.3 Experiment Results
	5 Conclusions
	References
Community Detection Supported by Node Embeddings (Searching  for a Suitable Method)
	1 Introduction
	2 Method Description
		2.1 Motivation
	3 Experiment Design
	4 Results
	5 Final Remarks
	References
Modeling Node Exposure for Community Detection in Networks
	1 Introduction
	2 Community Detection with Exposure
		2.1 Representing Exposure
		2.2 The Ground Truth Adjacency Matrix
		2.3 The Observed Adjacency Matrix
		2.4 Inference and Expectation-Maximization
	3 Results
		3.1 Synthetic Data
		3.2 Real Data
	4 Conclusions
	References
Community Detection for Temporal Weighted Bipartite Networks
	1 Introduction
	2 Methods
		2.1 Weighted Temporal Bipartite Network
		2.2 Projections of Weighted Temporal Bipartite Network
		2.3 Community Detection Methods
	3 Results
		3.1 Modularity
		3.2 Community Structure Comparison
	4 Conclusions
	References
Robustness and Sensitivity of Network-Based Topic Detection
	1 Introduction
	2 State of the Art
	3 Method and Material
	4 Results and Discussion
		4.1 The Effect of the Window Size
		4.2 Filters on the Word Co-occurrence Matrix
		4.3 Weighting Scheme
		4.4 Selection of the Community Detection Algorithm
	5 Conclusions
	References
Community Detection Using Moore-Shannon Network Reliability: Application to Food Networks
	1 Introduction
	2 Related Work
	3 Preliminaries
	4 Community Detection Framework
	5 Experimental Results
	6 Future Work
	References
Structural Network Measures
Winner Does Not Take All: Contrasting Centrality in Adversarial Networks
	1 Introduction
	2 Low-Key Leaders
	3 Data and Methods
		3.1 Dominance Networks
		3.2 Trade Networks
		3.3 Bitcoin Trust Networks
	4 Directed Ranking Model
	5 Discussion and Future Work
	References
Reconstructing Degree Distribution and Triangle Counts from Edge-Sampled Graphs
	1 Introduction
	2 Related Work
	3 Properties of Edge Sampled Graphs
		3.1 Degree
		3.2 Triangles
	4 Estimators for the Degree Sequence and Triangle Count
		4.1 Method of Moments Estimators
		4.2 Bayes Estimator
	5 Constructing a Prior
		5.1 Degree Distribution
		5.2 Triangle per Link Distribution
	6 Results
	7 Conclusion
	References
Generalizing Homophily to Simplicial Complexes
	1 Introduction
		1.1 Related Work
	2 Preliminaries
	3 Defining Group Homophily
		3.1 Homophily of Homogeneous Groups
		3.2 Homophily in Heterogeneous Groups
	4 Homophily in Network Data
	5 Homophily and Higher Order Link Prediction
	6 Conclusions
	References
Statistical Network Similarity
	1 Introduction
	2 Previous Work
	3 Methods
		3.1 Vertex-vertex Jaccard Distance
		3.2 Dissimilarity of Probability Distributions
	4 Numerical Results
	5 Conclusion
	References
Intersection of Random Spanning Trees in Small-World Networks
	1 Introduction
	2 Random Spanning Trees
		2.1 Minimum Expected Intersection
	3 Experiments
		3.1 Experiments on Random Model Networks
		3.2 Experiments on Real-Networks
	4 Conclusions and Future Work
	References
Node Classification Based on Non-symmetric Dependencies and Graph Neural Networks
	1 Introduction
	2 Dependency and Prominency
		2.1 Prominency Weight
	3 Datasets
	4 Node Prominency Classification
	5 Discussion
	6 Conclusion
	References
Mean Hitting Time of Q-subdivision Complex Networks
	1 Introduction
	2 Related Work
	3 Methodology
		3.1 Network Matrices Notation
		3.2 Random Walk Model on Networks
		3.3 Matrices Notation of Q-subdivision Network
		3.4 Hitting Time Calculation for Random Walks on Q-subdivision Network
	4 Mean Hitting Time for Random Walks on Q-subdivision Network
	5 Results and Analysis
		5.1 Scale-Free Property of q-subdivision Network
		5.2 Variation of Mean Hitting Time of Q-subdivision Network with q
	6 Conclusions and Future Work
	References
Delta Density: Comparison of Different Sized Networks Irrespective of Their Size
	1 Introduction
	2 Datasets
	3 Motivation: Analysis of Network Density and Average Degree
	4 Δ-Density
	5 Experiments
		5.1 Effect of Parameter δ
	6 Conclusion
	References
Resilence and Robustness of Networks
Robustness of Network Controllability with Respect to Node Removals
	1 Introduction
	2 Network Data
		2.1 Directed Synthetic Networks
		2.2 Real-World Networks
	3 Minimum Fraction of Driver Nodes Under Random Node Removals
		3.1 Analytical Approximation
		3.2 Validation
	4 Minimum Fraction of Number of Driver Nodes Under Targeted Node Removals
		4.1 Analytical Approximation
		4.2 Validation
	5 Conclusion and Discussion
	References
Optimal Network Robustness in Continuously Changing Degree Distributions
	1 Introduction
	2 Continuously Changing Degree Distributions
	3 Effect of Continuous Changes of Degree Distributions on Robustness
	4 Relation of Robustness and FVS in Changing Degree Distributions
	5 Effect of Chain-Like Structure Generated by the IPA Model on Robustness
	6 Conclusion
	References
Investments in Robustness of Complex Systems: Algorithm Design
	1 Introduction
	2 Model and Formulation
		2.1 Model
	3 Main Analysis
		3.1 Gradient Method
		3.2 Convex Relaxation
	4 Numerical Results
	5 Conclusion
	References
Incremental Computation of Effective Graph Resistance for Improving Robustness of Complex Networks: A Comparative Study
	1 Introduction
	2 Effective Graph Resistance
	3 RobGA{L+}: Incremental Computation of the Effective Graph Resistance
	4 Strategies for Link Addition
	5 Experimental Evaluation
		5.1 Networks
		5.2 Performance Measures
		5.3 Results
	6 Conclusion
	References
Analysis on the Effects of Graph Perturbations on Centrality Metrics
	1 Introduction
	2 Related Works
	3 Background
	4 Methods
	5 Experiments
		5.1 Datasets
		5.2 When a Perturbation is Small
		5.3 How the Centrality Metrics Vary in Probabilistic Failure Models
	6 Conclusions
	References
Robustness of Preferential-Attachment Graphs: Shifting the Baseline
	1 Introduction
	2 Preliminaries
		2.1 Graphs and Degree Sequences
		2.2 Network Robustness
		2.3 Network Models
	3 Theory
	4 Experiments
		4.1 Sensitivity to the Choice of the Fraction of Removed Vertices
		4.2 Sensitivity to the Choice of Parameters in the PA Model
		4.3 Consistency of Near-Optimal Robustness
	5 Conclusions
	References
The Vertex-Edge Separator Transformation Problem in Network-Dismantling
	1 Introduction
	2 The Transformation of the Dismantling Set of Nodes  and Edges
		2.1 Sub-Problem 1: Transformation From Edge Separator to Vertex Separator
		2.2 Sub-Problem 2: Transformation From Vertex Separator to Edge Separator
	3 Results
	4 Conclusion
	References
Network Analysis
Gig Economy and Social Network Analysis: Topology of Inferred Network
	1 Introduction
	2 Data and Methods
		2.1 Network Statistics
		2.2 Network Significance
		2.3 Bootstrap
	3 Results
	References
Understanding Sectoral Integration in Energy Systems Through Complex Network Analysis
	1 Introduction
	2 Sectoral Integration: A Definition
	3 Methodology
	4 Application to Actual Energy Systems
	5 Conclusion
	References
An Analysis of Bitcoin Dust Through Authenticated Queries
	1 Introduction
	2 Background
	3 Efficient Information Retrieval in Bitcoin
		3.1 Merkle Interval Tree
	4 Bitcoin Dust
	5 Experimental Results
		5.1 Tree Construction
		5.2 Transaction Analysis
		5.3 Address Analysis
		5.4 Output Analysis
	6 Conclusions and Future Work
	References
Optimal Bond Percolation in Networks by a Fast-Decycling Framework
	1 Introduction
	2 Materials and Methods
		2.1 Data Sets
		2.2 Classical Algorithms
		2.3 Evaluation of the Solutions to the OBP Problem
		2.4 The 2-core-based Framework and Algorithms to Approximate the OBP Problem
	3 Result and Discussions
	4 Conclusion
	References
Motif Discovery in Complex Networks
Integrating Temporal Graphs via Dual Networks: Dense Graph Discovery
	1 Introduction
	2 Definitions
		2.1 Graph Alignment Approach
		2.2 Finding Episodes in the Alignment Graphs
		2.3 The Densest Subgraph Problem
	3 Algorithms for K-Densest-Alignment-Episodes and k-l-Densest-Alignment-Episodes
		3.1 A Heuristic for K-Densest-Alignment-Episodes
	4 Experimental Analysis
	5 Conclusion
	References
Exploring and Mining Attributed Sequences of Interactions
	1 Introduction
	2 Related Work
		2.1 FCA and Closed Pattern Mining on Graphs
		2.2 Stream Graphs and Modelling of Interactions Over Time
	3 Closed Pattern Mining
		3.1 Core Closed Pattern Mining
		3.2 Exhibiting Patterns of Interest
	4 Stream Graphs
	5 Pattern Enumeration in Stream Graphs
		5.1 Core Operators and Calculation
		5.2 Pattern Enumeration and Pattern Set Selection
	6 Experiments
		6.1 Results
	7 Conclusion
	References
Air Transport Network: A Comparison of Statistical Backbone Filtering Techniques
	1 Introduction
	2 Backbone Extraction Techniques
		2.1 Disparity Filter ch43disparity:filter
		2.2 Polya Urn Filter ch43polya:urn:filter
		2.3 Marginal Likelihood Filter ch43mlf
		2.4 Noise Corrected Filter ch43noise:corrected:filter
		2.5 Global Statistical Significance Filter (GLOSS) ch43gloss:filter
		2.6 Locally Adaptive Network Sparsification Filter  (LANS) ch43lans:filter
		2.7 ECM Filter ch43ecm:filter
	3 Data and Method
		3.1 Data
		3.2 Methods
	4 Experimental Results
		4.1 Correlation of the Backbone Extraction Techniques
		4.2 Comparing the Backbone Extraction Techniques for the Same Significance Level
		4.3 Comparing the Backbone Extraction Techniques for Different Significance Levels
	5 Discussion
	6 Conclusion
	References
Towards the Concept of Spatial Network Motifs
	1 Introduction
	2 A Novel Concept of Spatial Motifs
	3 Finding and Counting Spatial Motifs
		3.1 Enumerating Subgraph Occurrences
		3.2 Subgraph Types and Canonical Labeling
	4 Experimental Results
		4.1 Results for ``Grid-Like'' Street Layouts
		4.2 Results for ``Non Grid-Like'' Street Layouts
		4.3 Comparison Between Cities
	5 Conclusions and Future Work
	References
Improving the Characterization and Comparison of Football Players with Spatial Flow Motifs
	1 Introduction
	2 Related Work
	3 Data Description
	4 Methodology
	5 Results
	6 Conclusions and Future Work
	References
Dynamics on/of Networks
Bayesian Approach to Uncertainty Visualization of Heterogeneous Behaviors in Modeling Networked Anagram Games
	1 Introduction
		1.1 Background and Motivation
		1.2 Novelty and Contributions
		1.3 Related Work
	2 State Transition Model and Extension
		2.1 State Transition Model
		2.2 Motivation for Model Extension
	3 Bayesian Uncertainty Visualization Method
	4 Visualization of Heterogeneous Behaviors
	5 Agent-Based Simulations of Networked Anagram Games and Results
		5.1 Simulation Process
		5.2 Visualization of Simulation Results
	6 Summary
	References
Understanding the Inter-Enterprise Competitive Relationship Based on the Link Prediction Method: Experience from Z-Park
	1 Introduction
	2 Data and Modelling
		2.1 Data Description
		2.2 ENMON Model
	3 Methodology
	4 Results and Discussion
		4.1 Link Prediction Results
		4.2 Evolutionary Mechanism
		4.3 Prediction of Potential Competition
	5 Conclusion
	References
Analyzing Configuration Transitions Associated with Higher-Order Link Occurrences in Networks of Cooking Ingredients
	1 Introduction
	2 Related Work
	3 Preliminaries
	4 Analysis Method
		4.1 Transition Analysis of Active Configuration
		4.2 Influence Analysis
	5 Experiments
		5.1 Datasets
		5.2 Results for Transition Analysis of Active Configuration
		5.3 Evaluation of Influence Analysis Model
		5.4 Results for Influence Analysis
	6 Conclusion
	References
Role of Network Topology in Between-Community Beta Diversity on River Networks
	1 Introduction
	2 Methods
		2.1 Neutral Model Implementation
		2.2 Beta Diversity
		2.3 Optimal Channel Networks
	3 Results
	4 Discussion and Conclusion
	References
Can One Hear the Position of Nodes?
	1 Introduction
		1.1 Summary of Contributions
	2 Related Work on Centrality Learning
		2.1 Traditional Machine Learning
		2.2 Graph Neural Networks
	3 Methods
		3.1 Network Auralization
		3.2 Centrality Learning as a Sound Recognition Problem
	4 Results and Discussion
		4.1 The Voice of Graphs and Nodes
		4.2 Centrality Learning Results
	5 Conclusion
	References
Memory Based Temporal Network Prediction
	1 Introduction
	2 Temporal Network Representation
	3 Empirical Data Sets
	4 Memory in Temporal Networks
	5 Temporal Link Prediction Methods
		5.1 Self-Driven (SD) Model
		5.2 Self- and Cross-Driven (SCD) Model
		5.3 Baseline Models
	6 Model Evaluation
		6.1 Link Prediction Quality
		6.2 Choice of Decay Factor
		6.3 Comparison of Models
	7 Model Interpretation
		7.1 Interpretation of SCD Model
		7.2 Decay Factor
	8 Conclusion
	References
Drug Trafficking in Relation to Global Shipping Network
	1 Introduction
	2 Datasets
	3 Methods
		3.1 Topological Metrics
		3.2 SIS Spreading Process
		3.3 Flow Optimization Model
		3.4 Regression Model
	4 Conclusion
	References