Algorithms and Models for the Web-Graph

Name: Algorithms and Models for the Web-Graph | 8th International Workshop, WAW 2011, Atlanta, GA, USA, May 27-29, 2011, Proceedings
Brand: Springer
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8th International Workshop, WAW 2011, Atlanta, GA, USA, May 27-29, 2011, Proceedings

Alan Frieze Paul Horn Pawel Pralat(Herausgeber*in)

Springer (Verlag)

Erschienen am 7. Juni 2011

IX, 127 Seiten

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978-3-642-21286-4 (ISBN)

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Inhalt

Title Page
Preface
Organization
Table of Contents
A Spectral Algorithm for Computing Social Balance
Introduction
Related Work
Basics
Balanced Triangles in Complete Networks
Balanced Triangles in Arbitrary Networks
Experiments
Evaluating Spectral on Real-World Signed Networks
Evaluating Spectral on Synthetic Signed Networks
Conclusions
References
High-Ordered Random Walks and Generalized Laplacians on Hypergraphs
Introduction
Definition of the s-th Laplacian
Case 1 = s = r/2
The Case r/2 & s = r - 1
Examples
Properties of Laplacians
Applications
The Random s-Walks on Hypergraphs
The s-Distances and s-Diameters in Hypergraphs
The Edge Expansions in Hypergraphs
Concluding Remarks
References
Detecting the Structure of Social Networks Using ($a, ß$)-Communities
Introduction
Preliminaries
Experimental Results
Social Graphs
Random Graphs
Conclusion
References
Latent Clustering on Graphs with Multiple Edge Types
Introduction
Contributions
An Illustrative Problem
Background
Clustering
Variation of Information of Clusterings
Previous Work
Searching the Space of Clusterings
Sampling the Clustering Space
Meta-clusters: Clusters of Clusterings
Efficient Representation of the Clusterings
Averaging Clusterings within a Cluster
Ordering by Set-Wise Information Content
Physics Articles from arXiv.org
Conclusion and Future Work
References
Quick Detection of Top-k Personalized PageRank Lists
Introduction
Monte Carlo Methods
Variance Based Performance Comparison and CLT Approximations
Convergence Based on Order
Solution Relaxation
References
Rank-Based Models of Network Structure and the Discovery of Content
Why Network Content Matters
A Rank Model of Content
Model Overview
Ranking by Age
Randomly Chosen Initial Rank
A Rank Model of Network Structure
The Discovery of Content through Structure
Conclusion
References
1-Local 33/24-Competitive Algorithm for Multicoloring Hexagonal Graphs
Introduction
Basic Definition and Useful Facts
Algorithm
Correctness Proof
Conclusion
References
Modeling Social Networks through User Background and Behavior
Introduction
The Model
Metrics
Results: Undirected Networks
Results: Directed Networks
Comparison with the Forest Fire Model
Some Analytical Observations
Conclusions and Open Problems
References
Dirichlet PageRank and Trust-Based Ranking Algorithms
Introduction
Related Work
Results in This Paper
Several Applications of Dirichlet PageRank
Preliminaries
PageRank with Dirichlet Boundary Conditions
Dirichlet PageRank with Given Boundary Conditions
Algorithms and Analysis
Applications of Dirichlet PageRank
Adjusting Spammers' Influence
Adjusting Rank Based on Trust
References
Efficient Generation of Networks with Given Expected Degrees
Introduction
Model Description
Erdos-Rényi Case
Chung-Lu Case
Efficiency
Examples
Discussion
References
Author Index

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