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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
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

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