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

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Intro -- Preface -- Organization -- Contents -- An Upper Bound on the Burning Number of Graphs -- 1 Introduction -- 2 Notations and Lemmas -- 3 Proof of Theorems1 and 2 -- References -- Assortativity in Generalized Preferential Attachment Models -- 1 Introduction -- 2 Generalized Preferential Attachment -- 2.1 Definition of the PA-Class -- 2.2 Power-Law Degree Distribution -- 2.3 Clustering Coefficient -- 3 Assortativity -- 4 Experiments -- 5 Proofs -- 5.1 Proof of Theorem3 -- 5.2 Proof of Theorem4 -- References -- Diclique Clustering in a Directed Random Graph -- 1 Introduction -- 1.1 Clustering in Directed Networks -- 1.2 A Directed Random Graph Model -- 1.3 Degree Distributions -- 1.4 Diclique Clustering -- 1.5 Diclique Versus Transitivity Clustering -- 2 Proofs -- References -- Distributed and Asynchronous Methods for Semi-supervised Learning -- 1 Introduction -- 2 Definitions and Problem Formulation -- 3 Distributed Approaches -- 3.1 Stochastic Approximation Approach -- 3.2 Randomized Kaczmarz Approach -- 3.3 Comments on Implementation -- 4 Application to Specific SSL Methods -- 4.1 Normalized Laplacian-Type Methods -- 4.2 Regularized Laplacian Method -- 4.3 Harmonic Functions Method -- 5 Experiments -- 5.1 WebKB Graph -- 5.2 US Football Graph -- 5.3 Gaussian Mixture Model Graph -- 5.4 Online Learning -- 5.5 Faster Convergence for Normalized Laplacian -- 6 Conclusion -- References -- Existence and Region of Critical Probabilities in Bootstrap Percolation on Inhomogeneous Periodic Trees -- 1 Introduction -- 2 Definitions and Preliminaries -- 2.1 Notation -- 3 Bootstrap Percolation -- 3.1 Bootstrap Percolation on an Oriented Tree "017E T -- 3.2 Proof of Theorem1 -- 3.3 Region of Full Percolation -- 3.4 Trajectory of "017E zt -- 3.5 Bootstrap Percolation on an Unoriented Tree T -- 4 Numerical Estimation of W0 -- 5 Conclusion -- References

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