Network Algorithms, Data Mining, and Applications: NET, Moscow, Russia, May 2018

✍ Scribed by Ilya Bychkov (editor), Valery A. Kalyagin (editor), Panos M. Pardalos (editor), Oleg Prokopyev (editor)

• Publisher: Springer Nature
• Year: 2020
• Tongue: English
• Leaves: 250
• Series: Springer Proceedings in Mathematics & Statistics
• Edition: 1
• Category: Library

✦ Synopsis

This proceedings presents the result of the 8th International Conference in Network Analysis, held at the Higher School of Economics, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government. 

Contributions in this book focus on the development of network algorithms for data mining and its applications. Researchers and students in mathematics, economics, statistics, computer science, and engineering find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks. Machine learning techniques in network settings including community detection, clustering, and biclustering algorithms are presented with applications to social network analysis.

✦ Table of Contents

Preface
References
Contents
Contributors
Network Algorithms
Fairness in Resource Allocation: Foundation and Applications
1 Introduction
1.1 Fairness in Communication Networks
1.2 Fairness in Facility Location
1.3 Fairness in Evacuation and Traffic Management
1.4 Fairness in Air Traffic Control (ATC)
1.5 Fairness in Job Scheduling
2 Fairness Early Development
3 Fairness Measures
3.1 Basic Fairness Measures
3.2 Gini Index
3.3 Jain's Index
3.4 Unfairness
4 Lexicographic Ordering
5 Max-Min Fairness
6 Proportional Fairness
7 (p, α) - Proportional Fairness
8 Price of Fairness
9 Concluding Remarks
References
Mixed Integer Programming for Searching Maximum Quasi-Bicliques
1 Introduction
2 Maximum Quasi-Cliques and Quasi-Bicliques
2.1 Basic Definitions
2.2 Maximum Quasi-Cliques
2.3 Maximum Quasi-Bicliques
3 Quasi-biclique Searching Models
3.1 Model 1
3.2 Model 2
4 Datasets
5 Experimental Verification
5.1 Implementation Description
5.2 Illustrative Examples
5.3 Comparison of Algorithms
6 Results and Conclusions
References
Graph Clustering Via Intra-Cluster Density Maximization
1 Introduction
2 Previous Work
3 Problem Formulation
3.1 Optimization Problem: Mean Intra-Cluster Density Maximization
3.2 Overcoming Common Degeneracies
4 Numerical Experiments
4.1 Meta-Heuristic Techniques
4.2 Preliminary Results
5 Conclusion and Future Work
References
Computational Complexity of SRIC and LRIC Indices
1 Introduction
2 Methodology
2.1 Mathematic Notions
2.2 Short-Range Interaction Centrality (SRIC)
2.3 Long-Range Interaction Centrality (LRIC)
3 Computational Complexity of SRIC and LRIC Indices
3.1 Computational Complexity of the SRIC Index
3.2 Computational Complexity of the LRIC Index
4 Experimental Results
4.1 Experimental Results on Complete Graphs
4.2 Experimental Results on Complete Graphs
5 Conclusion
References
A Survey on Variable Neighborhood Search Methods for Supply Network Inventory
1 Introduction
2 Variable Neighborhood Search
3 VNS Contributions to Supply Chain Optimization Problems
3.1 VNS for the ELSR
3.2 VNS for the MDLSRP
3.3 VNS for MLLSs
3.4 VNS for CLSPs
3.5 VNS for IRPs
3.6 VNS for LIRPs
3.7 VNS for Supplier Selection and Performance Evaluation in Inventory Control
3.8 VNS for Sustainable Order Allocation (inventory) and Sustainable Supply Chain
3.9 VNS for EOQ-Based Inventory Problems
3.10 VNS for Joint Replenishment Problems
3.11 VNS for Vendor Managed Inventory Problems
3.12 VNS for Multi-criteria Inventory Classification
4 Conclusions
5 Future Research Guidelines
References
Network Data Mining
GSM: Inductive Learning on Dynamic Graph Embeddings
1 Introduction
2 Notation and Basic Definitions
3 Related Research
3.1 Matrix Factorization Based Methods
3.2 Random Walk Based Methods
3.3 Graph Convolutional Networks and GAE
4 Inductive Learning Embeddings on Dynamic Graphs
5 Experimental Setup
5.1 Baseline
5.2 Datasets
5.3 Model Framework
5.4 Evaluation Metrics
5.5 Discussion
6 Conclusion
References
Collaborator Recommender System
1 Introduction and Related Work
2 Problem Statement
3 Data Description
4 Model Description
5 Results
6 Conclusion
7 Appendix
References
Visual Product Recommendation Using Neural Aggregation Network and Context Gating
1 Introduction
2 Decision-Making Using Neural Aggregation of Visual Data
3 Experimental Results
4 Conclusion and Future Work
References
Network Structure and Scheme Analysis of the Russian Language Segment of Wikipedia
1 Introduction
2 Related Work
3 Wikipedia Network Creation
4 Analysis of Nodes degree Distribution for Article Nodes
5 Methods for Clearing Diffusion of Tails of Nodes Degree Distributions
6 Elimination of the Source of Deviation for Out-Degree Probability Density Function
7 Conclusion
References
Indirect Influence Assessment in the Context of Retail Food Network
1 Introduction
2 Literature Review
3 Long-Range Interaction Centrality (LRIC)
4 Data Analysis and Results
5 Conclusion
References
Facial Clustering in Video Data Using Deep Convolutional Neural Networks
1 Introduction
2 Video Data Analysis
3 Experimental Results
4 Conclusion
References
Network Applications
The Existence and Uniqueness Theorem for Initial-Boundary Value Problem of the Same Class of Integro-Differential PDEs
1 Introduction
2 Auxiliary Assertions
3 The Main Result
4 The Properties of the Studied Class of Integro-Differential Equations
References
Mapping of Politically Active Groups on Social Networks of Russian Regions (On the Example of Karachay-Cherkessia Republic)
1 Introduction
1.1 Description of the Research Problem
1.2 Research Problems of Information Gathering on Social Networks in the KChR
2 Literature Review of the Regional Network Researches
2.1 Network Approaches to Regional Political Mapping on the Internet
2.2 Regional Policy Network Studies
3 A Brief Description of the Mapping of Social Networks Using Grain Clustering Algorithm
4 Structuring Clusters of Politically Active Groups of KChR on Facebook
4.1 General Clustering of Groups that Show Political Activity in the Information Space of the KChR and Neighboring Caucasian Regions
4.2 Cluster of Politically Active Groups of KChR
5 Conclusion
References
Social Mechanisms of the Subject Area Formation. The Case of “Digital Economy”
1 Introduction
2 Literature Review
2.1 Approaches in the Modern Conceptual Analysis
2.2 Expert Analysis of the “Digital Economy” Field Formation
2.3 Ideological Character of the Term “Digital Economy”
3 Data Processing with the Help of Automatic Networks
3.1 Toolkit for the Text Analysis
3.2 Conceptual Statistical Analysis of Texts
4 Empirical Results
5 Conclusion
References
Methodology for Measuring Polarization of Political Discourse: Case of Comparing Oppositional and Patriotic Discourse in Online Social Networks
1 Introduction
2 Literature Review of Discourse Research and Allocating Key Speech Markers
2.1 Theoretical Background for the Study of Discourse
2.2 Studies of Political Language Features
2.3 Linguistic Features of Measuring Distinctive Words
3 Method for Differentiating Speech Markers
3.1 Data Description
3.2 Characteristics of Frequency Distributions
3.3 Methodological Issues
4 Practical Results of the Study
5 Conclusion
5.1 Informative Conclusions
5.2 Methodological Conclusions
5.3 Final Thoughts
References
Network Analysis Methodology of Policy Actors Identification and Power Evaluation (The Case of the Unified State Exam Introduction in Russia)
1 Introduction
2 Methodology
3 Application
4 Conclusion
References