Algorithms and Models for the Web Graph - Info and Reading Options

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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 (α, β)-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

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