Algorithms and Models for the Web Graph: 15th International Workshop, WAW 2018, Moscow, Russia, May 17-18, 2018, Proceedings - Info and Reading Options

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Intro -- Preface -- Organization -- Contents -- Finding Induced Subgraphs in Scale-Free Inhomogeneous Random Graphs -- 1 Introduction -- 1.1 Model -- 1.2 Algorithms -- 2 Proof of Theorems 1 and 2 -- 3 Experimental Results -- 3.1 Real Network Data -- 4 Conclusion -- References -- The Asymptotic Normality of the Global Clustering Coefficient in Sparse Random Intersection Graphs -- 1 Introduction -- 2 Proofs -- 3 Auxiliary Results -- References -- Clustering Properties of Spatial Preferential Attachment Model -- 1 Introduction -- 2 Spatial Preferential Attachment Model -- 2.1 Definition -- 2.2 Properties of the Model -- 3 Clustering Coefficient -- 4 Results -- 4.1 Notation -- 4.2 Results -- 5 Experiments -- 5.1 Algorithm -- 5.2 Empirical Analysis of the Local Clustering Coefficient -- References -- Parameter Estimators of Sparse Random Intersection Graphs with Thinned Communities -- 1 Introduction -- 2 Model Description -- 3 Analysis of Local Model Characteristics -- 3.1 Sparse Parameter Regime -- 3.2 Subgraph Densities -- 3.3 Model Transitivity -- 3.4 Degree Mean and Variance -- 4 Parameter Estimation of Sparse Models -- 4.1 Empirical Subgraph Counts -- 4.2 Parameter Estimation in the Bernoulli Model -- 5 Numerical Experiments -- 5.1 Attainable Regions in the Bernoulli Model -- 5.2 Real Data -- 6 Technical Proofs -- 6.1 Analysis of Link Density -- 6.2 Analysis of 2-Star Covering Density -- 6.3 Analysis of Triangle Covering Density -- 6.4 Analysis of Model Transitivity -- 6.5 Analysis of Degree Moments -- 6.6 Analysis of Observed Link Density -- 6.7 Analysis of Observed 2-Star Covering Density -- 6.8 Analysis of Observed Triangle Density -- References -- Joint Alignment from Pairwise Differences with a Noisy Oracle -- 1 Introduction -- 2 Theoretical Preliminaries -- 3 Proposed Method -- 4 Related Work -- 5 Conclusion -- References

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