Complex Networks & Their Applications X

Name: Complex Networks & Their Applications X | Volume 2, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021
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Volume 2, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021

Rosa Maria Benito Chantal Cherifi Hocine Cherifi Esteban Moro Luis M. Rocha Marta Sales-Pardo(Editor)

Springer (Publisher)

Published on 1. January 2022

XXVIII, 819 pages

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Content

Intro
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
Information Spreading in Social Media
Hate Speech Detection on Social Media Using Graph Convolutional Networks
1 Introduction
2 Related Work
3 The Proposed Approach
3.1 Proposed Model Overview
3.2 Social Network Encoder as Unified Feature Encoder
4 Experiments and Results
4.1 Experiments Overview
4.2 Dataset
4.3 Experiments Setting
4.4 Experimental Results
5 Discussion
5.1 Error Analysis
5.2 Investigating the Effect of Text Encoders
6 Conclusion
References
Capturing the Spread of Hate on Twitter Using Spreading Activation Models
1 Introduction
2 Related Work
3 Central Idea
3.1 Classification
3.2 The Native SPA Model
3.3 Topic Modelling
3.4 The Original TopSPA
3.5 Modified TopSPA
4 Experiments and Results
4.1 Dataset and Preprocessing
4.2 Annotation
4.3 Hate Classification Results
4.4 Data Segmentation
4.5 SPA Based Results for Hateful User Detection
4.6 Topic Modelling
4.7 Top SPA Results
5 Conclusion
References
Indirect Causal Influence of a Single Bot on Opinion Dynamics Through a Simple Recommendation Algorithm
Abstract
1 Introduction
2 Methods
2.1 Overview
2.2 Simulation Procedure
3 Results
3.1 Population-Level Influence of the Bot on the Average Opinion
3.2 Magnitude of Direct Bot Influence on Individual Agents
3.3 Individual-Level Shift in Opinion as a Result of Direct and Indirect Bot Influence
4 Discussion
Supplementary Materials
References
Modelling the Effects of Self-learning and Social Influence on the Diversity of Knowledge
1 Introduction
2 Methods
2.1 Model
2.2 Diversity Metric
2.3 Code Availability
3 Results
3.1 Nonblock Intralayer Models
3.2 Block Intralayer Models and Topic Group Diversity
4 Discussion
References
Activator-Inhibitor Model for Describing Interactions Between Fake News and Their Corrections
1 Introduction
2 Reaction-Diffusion System
3 Activator-Inhibitor Model for Fake News and Corrections
3.1 Basic Concept
3.2 Activator-Inhibitor Model on Networks
4 Experimental Evaluation
5 Conclusion
References
Love and Hate During Political Campaigns in Social Networks
1 Introduction
2 Detecting Love and Hate
3 A Case Study: The 2016 USA Elections
3.1 The Dataset
3.2 Sentiment Analysis of Tweets
3.3 User Opinion About Each Candidate Separately
3.4 Polarization
3.5 Relating Opinions About Both Candidates
3.6 Love-Hate Diagram
4 Conclusions
References
Homophily - a Driving Factor for Hate Speech on Twitter
1 Introduction
2 Related Work
3 Central Idea
4 A Novel Familiarity Computation Approach
4.1 Graph Encoder
4.2 Computing Familiarity
4.3 Hateful Forms Detection Using Topic Modelling
5 Experiments
5.1 Experiments Overview
5.2 Experiments Settings
5.3 Experiments Results
6 Conclusion
References
Influencing the Influencers: Evaluating Person-to-Person Influence on Social Networks Using Granger Causality
1 Introduction
2 Background
2.1 Data and Definitions
3 Methods
3.1 Gathering Data
3.2 Identifying Topics
3.3 Granger Causality
4 Results
5 Discussion
5.1 Future Work
References
How the Far-Right Polarises Twitter: `Hashjacking' as a Disinformation Strategy in Times of COVID-19
1 Introduction
2 Background
3 Hypotheses
4 Research Design
5 Results
6 Conclusion
7 Appendix
References
Models of Influence Spreading on Social Networks
1 Introduction
2 Simple and Complex Contagion Models
3 Influence Spreading Models
3.1 Simple Contagion (Sim-sc)
3.2 Complex Contagion (Alg-cc)
3.3 Diffusion (Diff-sc)
4 Applications
5 Conclusions
References
Bubble Effect Induced by Recommendation Systems in a Simple Social Media Model
1 Introduction
2 The Model
2.1 Recommender Systems
2.2 The Procedure
2.3 Cluster Analysis
3 Results
4 Conclusions
References
Maximum Entropy Networks Applied on Twitter Disinformation Datasets
1 Introduction
2 Methods
2.1 Interaction Networks
2.2 Maximum Entropy Networks
2.3 Toy Example
2.4 Data
3 Results
3.1 Twitter Information Operations Datasets
3.2 Plandemic Dataset
4 Discussion
4.1 Limitations
5 Conclusions and Future Work
References
Community Deception in Networks: Where We Are and Where We Should Go
1 Introduction
2 Community Deception in Networks
3 Community Deception: Where We Are
3.1 Modularity-Based Deception
3.2 Safeness-Based Deception
3.3 Permanence-Based Deception
4 Evaluating Community Deception Algorithms
4.1 Deception Score Evaluation
5 Community Deception: Where We Should Go
References
Mitigating the Backfire Effect Using Pacing and Leading
1 Introduction
2 Experiment Design
3 Results
4 Discussion and Conclusion
References
Exploring Bias and Information Bubbles in YouTube's Video Recommendation Networks
Abstract
1 Introduction
2 Literature Review
3 Data and Methods
4 Analysis and Results
5 Conclusions and Future Work
Acknowledgments
References
Diffusion and Epidemics
Modeling the Spread of COVID-19 Over Varied Contact Networks
1 Introduction
2 Background
2.1 Graphs
2.2 Epidemiological Terminology
2.3 RT-PCR Based Tests
3 Related Works
4 Network Descriptions
5 Simulation
6 Intervention Strategies
7 Results
8 Evaluation
8.1 Testing Using Graph Properties
9 Conclusion
10 Future Work
References
Benchmarking Optimal Control for Network Dynamic Systems with Plausible Epidemic Models
1 Generating Model Instances of Varying Scale and Resolution
1.1 The Data Set
1.2 Distributing Air Traffic to Population Units
1.3 Aggregating the Nodes and Their Connections
1.4 Instances from Census Tracts in Oregon and Washington States
2 Metapopulation Network SIR Model
2.1 Network SIR Model
3 Network SIR with ``Social Distancing'' Control
3.1 Minimizing J[] Through Pontryagin Maximum Principle (PMP)
3.2 Numerical Solution for Two-Point Boundary Value Problem
3.3 Experiment: Patient Zero in Baker County, Oregon
4 Conclusion
References
Hardness Results for Seeding Complex Contagion with Neighborhoods
1 Introduction
2 Spreading Behavior in Complex Contagions
2.1 Lower Bound for a 2-Contagious Set
3 Neighborhood-TSS
3.1 Inapproximability Results
4 Conclusion
References
Influence Maximization in Complex Networks Through Supervised Machine Learning
1 Introduction
2 Related Work
2.1 Influence Mining
2.2 Machine Learning
3 Design and Methodology
3.1 Training Data Sets
3.2 Test Data Sets
3.3 Influential Node Identification
3.4 Structural and Nodal Attributes
4 Experiments and Results
4.1 Machine Learning Models
4.2 Results
5 Conclusion and Future Work
References
Story of Two Populations in Epidemics: Is Every Infection Counted?
1 Introduction
2 Setup
3 Approach and Model
3.1 Transmissibility Function
3.2 Discrete-Time Approximation of Transmissibility Function
4 Parameter Estimation and Model Selection
4.1 Model Selection
5 Case Study Using Real Data: COVID-19
5.1 COVID-19 Outbreak Data Used in the Study
5.2 Summary of Numerical Results
6 Conclusion
References
A Framework for Simulating Multiple Contagions Over Multiple Networks
1 Introduction
1.1 Background and Motivation
1.2 Our Contributions
1.3 Related Work
2 Multi-contagion Graph Dynamical System
2.1 Multiple Contagion GDS Formalism
2.2 Example MCGDS
3 CSonNet Modeling and Simulation Software System
3.1 Overview of Simulation Steps
3.2 Agent State Transition Models from Rules
4 Performance Evaluation
4.1 Networks
4.2 Strong Scaling Results
5 Case Studies
5.1 Threshold-SIR Two-Contagion Model and Simulations
5.2 SIR1-SIR2 Two-Contagion Model and Simulations
References
Finding Influential Nodes in Complex Networks Using Nearest Neighborhood Trust Value
1 Introduction
1.1 Related Work
2 Nearest Neighbourhood Trust Page Rank
3 Implementation
4 Results
4.1 Correlation of NTPR with Different Measures
4.2 Comparison Between Infection Rate and Centrality Value:
4.3 Comparison of Performance NTPR with Various Measures:
5 Conclusions
References
Two-Population SIR Model and Strategies to Reduce Mortality in Pandemics
1 Introduction
2 Two-Population SIR Model
3 Merged SIR Model to Reduce Mortality
4 Two-Population SIR Epidemic on Large Complex Networks
5 Conclusions
References
Hypergraph Laplacians in Diffusion Framework
1 Introduction
2 Preliminaries and Background
2.1 Graphs and Hypergraphs
2.2 Simplicial Laplacians
3 Hypergraph Laplacians
3.1 A Hypergraph Laplacian Between Fixed Dimension Hyperedges
3.2 A Generalized Hypergraph Laplacian
4 Experiments
5 Conclusion
References
Lumping Reductions for Multispread in Multi-Layer Networks
1 Introduction
2 Background
2.1 Reaction Networks
2.2 Multiplex Multi-Layer Networks
2.3 Lumping Species in a Reaction Network
2.4 Lumping Actors in Multiplex Networks
3 Results
3.1 Interacting Spreading Processes on Multiplex Multi-Layer Networks
3.2 The Multispread Model
3.3 Experimental Setup
3.4 Size of the Reduction
3.5 Cost of the Reduction
3.6 Speeding up Stochastic Simulations
3.7 Approximation of the Reduction
3.8 Reduction at the Level of Actors
4 Conclusions and Future Works
References
Microscopic Markov Chain Approach for Measuring Mobility Driven SARS-CoV-2 Transmission
1 Introduction
2 Technical Details
3 Florida and Mobility During the Pandemic
3.1 Data and Parameter Estimates
3.2 Residuals: True Estimates and the Markov Model
3.3 Alternate Realities
4 Discussion
References
Epidemics in a Synthetic Urban Population with Multiple Levels of Mixing
1 Introduction
2 Models, Materials and Methods
3 Epidemic Threshold, Evolution and Reproduction Number
4 Geographic Spreading and Predictability
5 Conclusion
References
Dynamics on/of Networks
From Mean-Field to Complex Topologies: Network Effects on the Algorithmic Bias Model
1 Introduction
2 Algorithmic Bias: From Mean-Field to Complex Topologies
3 Experimental Analysis and Results
4 Conclusion
References
Towards Control of Opinion Diversity by Introducing Zealots into a Polarised Social Group
1 Introduction
2 Related Literature
3 The Voter Model with Zealots
4 Expected Opinion Diversity at Equilibrium
4.1 Empirical Approximation
5 Control of Opinion Diversity
5.1 Backfire Effect
6 Numerical Experiments
7 Conclusion and Future Work
References
Sensing Enhancement on Complex Networks
1 Introduction
2 Model and Mean-Field Results
3 Numerical Results for Complex Networks
4 Conclusions
References
Cascading Failures and the Robustness of Cooperation in a Unified Scale-Free Network Model
1 Introduction
2 Evolutionary Game Dynamics
2.1 Game Playing and Payoffs
2.2 Tolerance and Agent Removal
2.3 Strategy Update
2.4 Results from Existing Literature
3 Network Topology
3.1 The Klemm-Eguíluz Growing Network Model
3.2 Network Parameters
4 Experiments
4.1 Brief Examples of Simulations
4.2 Survival Rate w.r.t.
4.3 Survival Rate w.r.t. and b
4.4 Survival Rate w.r.t. m
4.5 Extinction Boundary w.r.t. m
5 Final Remarks
References
Effect of Peer Influence and Looting Concerns on Evacuation Behavior During Natural Disasters
1 Introduction
2 Family Social Contact Network
3 Models
3.1 Network Model
3.2 Peer Influence Contagion Models
4 Simulations and Results
4.1 Simulation Description and Parameters
4.2 Simulation Results
5 Conclusions
References
Eigenvalues of Random Signed Graphs with Cycles: A Graph-Centered View of the Method of Moments with Practical Applications
1 Introduction
2 Cycles and Moments of the Eigenvalue Distribution
3 Random Networks with Short Cyclic Motifs
4 Circulant Directed Networks
5 Application: Reservoir Computing
5.1 Frequencies and Short Cycles
5.2 Memory Maximization in Circulant Matrices
6 Future Works
7 Conclusion
A Appendix
A.1 Reservoir Computing
A.2 Poles and Eigenvalues
References
Bosonic Random Walk Neural Networks for Graph Learning
1 Introduction
2 Preliminaries
2.1 Bosonic Quantum Mechanics
2.2 Quantum Walks
3 Bosonic Walks Networks
3.1 Bosonic Quantum Walks
3.2 Bosonic Quantum Walk Neural Networks
4 Experiments
5 Related Work
6 Conclusion
References
Machine Learning and Networks
Multiple Role Discovery in Complex Networks
1 Introduction
2 Related Work
2.1 Role Discovery
2.2 Multi-label Classification
3 Previous Method (STV Method) ch35kikuta2020framework
3.1 Acquisition of Node Representation
3.2 Domain Adversarial Learning
3.3 Model Selection Using Validation Network
4 Proposed Method
4.1 Multi-label Classification in Neural Networks
4.2 Creation of Training Data Using Community Extraction
5 Evaluation Experiments on Real-World Networks
5.1 Comparison Method
5.2 Variations of Proposed Method
5.3 Evaluation Metrics
5.4 Wikipedia Network
5.5 Blogcatalog Network
6 Conclusion
References
Graph Summarization with Latent Variable Probabilistic Models
1 Introduction
1.1 Motivation
1.2 Related Work
1.3 Significance and Novelty
2 Proposed Algorithm
2.1 Problem Setting
2.2 Code-Lengths for Encoding Graphs
2.3 Algorithm of GSL
3 Experiments
3.1 Synthetic Dataset
3.2 Real Dataset
4 Conclusion
A MDL Principle
References
Image Keypoint Matching Using Graph Neural Networks
1 Introduction
2 Related Work
3 Graph Neural Networks for Image Keypoint Matching
3.1 Local Feature Matching
3.2 Neighborhood Consensus
4 Experiments
4.1 Datasets
4.2 Baselines
4.3 Experimental Setup
4.4 Results
5 Conclusion
References
Relations Between Entropy and Accuracy Trends in Complex Artificial Neural Networks
1 Introduction
2 Materials and Methods
2.1 Background
2.2 Dataset
2.3 Methodology
3 Discussion and Conclusions
References
Curved Markov Chain Monte Carlo for Network Learning
1 Introduction
2 Overview: Discrete Curvature and Random Sampling
2.1 Discrete Curvature on Networks: Graph Forman Curvature
2.2 Random Sampling: Markov Chain Monte Carlo
3 Forman-Curved Markov Chain Monte Carlo
3.1 the Edge-Based Approach
3.2 The Node-Based Approach
3.3 Network Statistics
4 Implementation and Results
4.1 Performance Evaluation
4.2 Application: Deterministic Real-World Networks
5 Discussion
References
Extracting Semantic Information from Dynamic Graphs of Geometric Data
1 Introduction
2 Related Work
2.1 Trajectory Prediction
2.2 Role Discovery
2.3 Network Analysis
3 Semantic Geometric Pipeline
3.1 Geometric Data
3.2 Dynamic Passing Networks
3.3 Jump Markov Model
3.4 A Transformer
4 Results
4.1 Markovian Property
4.2 Jump Markov Model
4.3 Translating with Transformer
5 Conclusions
References
Graph-based Retrieval for Claim Verification over Cross-document Evidence
1 Introduction
2 Method
3 Experimental Analysis
4 Related Work
5 Conclusion
References
Supervised Link Weight Prediction Using Node Metadata
1 Introduction
2 Problem Definition and Comparison Metrics
2.1 Problem Definition
2.2 Comparison Metrics
3 Supervised Learning Methods
4 Proposed Features
4.1 Metadata Similarity Features: Incorporating Node Metadata
4.2 Topological Features
5 Experimental Results
5.1 Data Description
5.2 Results
6 Conclusion
References
Graph Embedding
Joint Use of Node Attributes and Proximity for Node Classification
1 Introduction
2 Problem Setting
3 Our Approach
3.1 Model
3.2 Algorithms
4 Experiments
4.1 Node Classification on Synthetic Data
4.2 Node Classification on Real-World Data
5 Conclusion
References
Online Updates of Knowledge Graph Embedding
1 Introduction
2 Problem Formulation
3 Embedding Learning from Scratch
4 Online Embedding Updates
5 Experimental Results
5.1 Experimental Setup
5.2 Accuracy and Efficiency Results
6 Conclusions
References
High-Speed and Noise-Robust Embedding of Hypergraphs Based on Double-Centered Incidence Matrix
1 Introduction
2 Related Work
2.1 Embedding
2.2 Clustering
3 Proposed Method
3.1 Embedding
3.2 Clustering
4 Experimental Settings
4.1 Datasets
4.2 Evaluation Measure
4.3 Methods Used for Comparison
5 Evaluation Results
5.1 Quality of Clustering
5.2 Efficiency of Embedding
6 Conclusion
References
Simple Negative Sampling for Link Prediction in Knowledge Graphs
1 Introduction
2 State-of-Art
3 The Proposed SNS Method
4 Experiments
4.1 Datasets
4.2 Evaluation Metrics
4.3 Results and Discussion
5 Conclusion
References
Evaluation of Anomaly Detection for Cybersecurity Using Inductive Node Embedding with Convolutional Graph Neural Networks
1 Introduction
2 State of the Art
3 Requirements
4 Anomaly Detection Using Inductive GNNs
5 Implementation
6 Evaluation
7 Discussion
8 Conclusions and Perspectives
References
Hybrid Graph Embedding Techniques in Estimated Time of Arrival Task
1 Introduction
2 Related Work
3 Data
4 Methods
5 Results
5.1 Implementation Details
5.2 Experiments
6 Conclusion and Outlook
References
What Is Learned in Knowledge Graph Embeddings?
1 Introduction
1.1 Definitions and Basic Properties of KGs
1.2 Embedding Models
1.3 Tasks and Evaluation
1.4 Do KG Models Learn Rules?
1.5 An Issue with Evaluating Large KGs
1.6 Summary of Our Contributions
1.7 Related Work
2 Three Mechanisms of KG Learning
2.1 Unstructured Statistical Learning
2.2 Network Learning
2.3 Inference of Rules by Learning Motifs
2.4 Distinguishing the Three Types of Learning
3 Synthetic KGs
3.1 Link Completion Without Motif Learning
3.2 Motif Factor with Noise
4 Experiments on Real KGs
4.1 Interpretation
5 Conclusions
References
Biological Networks
CUBCO: Prediction of Protein Complexes Based on Min-cut Network Partitioning into Biclique Spanned Subgraphs
Abstract
1 Introduction
2 Results
2.1 CUBCO Predicts Protein Complexes Using Min Cut
2.2 Comparative Performance of CUBCO Without Link Prediction
2.3 Integrating Link Prediction to Improve Performance of CUBCO
2.4 Prediction of New Protein Complexes in Conserved PPI Networks
3 Method
3.1 Contending Approaches for Prediction of Protein Complexes
3.2 PPI Networks and Gold Standards of Protein Complexes
4 Conclusion
References
Quantifying Cellular Pluripotency and Pathway Robustness Through Forman-Ricci Curvature
1 Introduction
2 Background
2.1 Motivation: Interplay of Entropy and Curvature
2.2 Biological Graphs: Topological and Geometric Information
2.3 Forman-Ricci Curvature
3 Approach
3.1 Gene Expression Pre-processing to Graph Construction
3.2 Gene Network Curvature Analysis
4 Results
4.1 Differentiation Cell Type (GSE75748)
4.2 Melanoma (GSE72056)
5 Conclusions
References
Emergence of Stable Functional Cliques in Developing Neural Networks
1 Introduction
2 MEA Data, Population Bursts and Correlations
3 Monoplex and Multiplex Networks
4 Multiplex Network Measures
4.1 Average Number of k-Cliques
4.2 k-Clique Persistence
5 Results
5.1 Average Number of k-Cliques, k=2,3,4
5.2 k-Clique Persistence for k=2,3,4
6 Discussion
References
Complex Networks Reveal Biological Functions of START Domains in Rice: Insights from Computational Systems Biology
Abstract
1 Introduction
2 Methodology
2.1 Selection and Classification of START Protein-Encoding Genes in Rice
2.2 Identification of Putative Co-expressing and Interacting Partners for START Genes
2.3 Identification of cis-Regulatory Elements and Construction of Gene Regulatory Networks (GRN)
3 Results
3.1 Co-expression Network of START Genes in Rice
3.2 Protein-Protein Interaction Networks of START Genes in Rice
3.3 Gene Regulatory Networks of START Genes in Rice
4 Conclusion and Future Perspective
Funding
References
Supervised Gene Function Prediction Using Spectral Clustering on Gene Co-expression Networks
1 Introduction
2 Preliminaries
2.1 Gene Co-expression Network
2.2 Gene Function Prediction
2.3 Spectral Clustering
2.4 SHAP
3 Clustering-Based Function Prediction
3.1 Affinity Graph Creation
3.2 Gene Enrichment with Clustering
3.3 Feature Selection
3.4 Training and Prediction
4 Case Study: Zea Mays
4.1 Data Description and Feature Selection
4.2 Benchmarks
4.3 Summary of Results
5 Concluding Remarks and Future Work
References
Analysis of the San Luis Obispo Bay Microbiome from a Network Perspective
1 Introduction
2 Background
3 Related Works
4 Materials and Methods
5 Results
6 Conclusion
References
From Quantitative SBML Models to Boolean Networks
1 Introduction
2 Boolean Networks and Their Synthesis
2.1 Definitions
2.2 Synthesis of BNs Compatible with a Structure and a Dynamics
3 Description of the SBML2BN Pipeline
3.1 Complete Quantitative SBML Models in a Nutshell
3.2 Extraction of the PKN from the SBML Model
3.3 Extraction of the Time-Series from the SBML Model
3.4 Boolean Networks Synthesis
3.5 Evaluation of Synthesised Boolean Networks
3.6 Pipeline Implementation
4 Evaluation of the SBML2BN Pipeline
4.1 Evaluation on the Running Example Eq. (1)
4.2 Evaluation on SBML Models from BioModels
5 Conclusion and Perspectives
References
Can Dynamic Functional Connectivity Be Used to Distinguish Between Resting-State and Motor Imagery in EEG-BCIs?
1 Introduction
2 Materials and Methods
2.1 Dataset
2.2 Preprocessing
2.3 ERD/S Computation
2.4 Exclusion Criteria
2.5 Functional Connectivity Evaluation
2.6 Static and Dynamic Functional Connectivity
3 Results
3.1 ERD/S and Static FC Classification Performances
3.2 Dynamic ERD/S and Functional Connectivity Classification Performance
4 Discussion and Conclusions
References
Network Medicine
Inferred Networks and the Social Determinants of Health
1 Introduction
2 Related Work
3 Methods
3.1 The LMSM-CI Dataset
3.2 Data Curation
3.3 Graph Inference
3.4 Clustering
3.5 Internal Evaluation Measures
4 Results
4.1 Cluster Evaluation
4.2 Cluster Properties
5 Discussion and Conclusion
References
Network-Based Analysis of Prescription Opioids Dispensing Using Exponential Random Graph Models (ERGMs)
Abstract
1 Introduction
2 Methods
2.1 Data Source
2.2 Study Population
2.3 Network-Based Framework
2.4 Selecting a Centrality Measure
2.5 Statistical Network Modeling
3 Results
3.1 Network Description
3.2 Predictors of High Betweenness Centrality
3.3 Predictors of Network Ties Between Patients
4 Discussion
4.1 Limitations
5 Conclusions
Funding
References
Drug Repositioning Using Multiplex-Heterogeneous Network Embedding: A Case Study on SARS-CoV2
1 Introduction
2 Related Work
3 Method
3.1 Random Walk with Restart on Multiplex-Heterogeneous Network (RWR-MH)
3.2 Learning Objective
4 Experimental Setup
4.1 Datasets
4.2 Evaluation of the Approach
5 Experimental Results
5.1 Results on Link Prediction
5.2 Cluster Analysis of SARS-CoV2 Proteins
6 Conclusion and Perspectives
References
Drug Repurposing Using Link Prediction on Knowledge Graphs with Applications to Non-volatile Memory
1 Introduction
2 Dataset
3 Model Architecture
4 Output Interpretation
5 Collaborative Filtering
6 Rank Aggregation Using Non-volatile Memory
6.1 Interacting with Optane-PM
7 Experiments
7.1 Link Prediction
7.2 Scalable Drug Rank Aggregation
8 Evaluation
8.1 Link Prediction and Drug Aggregation Performance
8.2 Scalability Results
9 Conclusion
References
Urban Systems and Networks
Understanding Imbalance Mechanisms in Shared Mobility Systems
1 Introduction
2 Walking in the Simplex: Microstates
2.1 Macrostates
2.2 System Collapse
2.3 Analytical Limit of the Fundamental Matrix for Random Walks
3 Assymetry and Super-Difussion. The Effect of Loops
4 Conclusion
References
Neighborhood Discovery via Network Community Structure
1 Introduction
2 Data Sets
3 Analysis Methods
4 Results for Central Tokyo
5 Conclusions and Future Work
References
CityChrone: an Interactive Platform for Transport Network Analysis and Planning in Urban Systems
1 Introduction
2 Related Works
3 Accessibility Quantities
4 Routing Algorithm
5 Urban Tessellation and Data Preprocessing
6 The CityChrone Platform
7 Preliminary Analysis of New Public Transport Network Scenarios: The Case of Rome
8 Conclusion
References
VLT-LUT: Modeling the Very Long-Term Evolution of the City in 300 Years
1 Introduction
2 Very Long-Term Land Use and Transportation Model
2.1 Land Use Model: CUBE Land
2.2 Transportation Model: Markovian Traffic Equilibrium
2.3 Demographic and Firmographic Models
2.4 Trip Generation and Distribution Model
3 Simulation Process
3.1 Modeling the Urban Spatial Border
3.2 Solution Algorithm
4 Simulation Results
5 Final Comments and Conclusions
References
Layered Hodge Decomposition for Urban Transit Networks
1 Introduction and Background
1.1 Transit Systems Flow and the Route Assignment Problem
1.2 Data
2 Methodology
2.1 Hodge Decomposition
2.2 Layered Hodge Decomposition and Flow Prediction
3 Exploratory Analysis Using London Underground Data
3.1 Discussion and Conclusion
References
Correction to: Complex Networks & Their Applications X
Correction to: R. M. Benito et al. (eds.): Complex Networks & Their Applications X, SCI 1073, https://doi.org/10.1007/978-3-030-93413-2
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