Algorithms and Models for the Web Graph - Info and Reading Options
15th International Workshop, WAW 2018, Moscow, Russia, May 17-18, 2018, Proceedings
By Anthony Bonato
"Algorithms and Models for the Web Graph" is published by Springer in May 30, 2018 - Cham and it has 194 pages.
“Algorithms and Models for the Web Graph” Metadata:
- Title: ➤ Algorithms and Models for the Web Graph
- Author: Anthony Bonato
- Number of Pages: 194
- Publisher: Springer
- Publish Date: May 30, 2018
- Publish Location: Cham
“Algorithms and Models for the Web Graph” Subjects and Themes:
- Subjects: ➤ Computational complexity - Discrete Mathematics in Computer Science - Data Mining and Knowledge Discovery - Information organization - Computer networks - Data mining - Algorithm Analysis and Problem Complexity - Information retrieval - Information Systems Applications (incl. Internet) - Computer science - Information storage and retrieval systems - Computer software - Computer algorithms - Computer graphics - World wide web - Computer Communication Networks
Edition Specifications:
- Format: paperback
Edition Identifiers:
- The Open Library ID: OL28237238M - OL19825597W
- ISBN-13: 9783319928708 - 9783319928715
- ISBN-10: 3319928708
- All ISBNs: 3319928708 - 9783319928708 - 9783319928715
AI-generated Review of “Algorithms and Models for the Web Graph”:
"Algorithms and Models for the Web Graph" Description:
Open Data:
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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