Algorithms and models for the web graph : 11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014 : proceedings / Anthony Bonato, Fan Chung Graham, Paweł Prałat (Eds.).

By: (11th : WAW (Workshop) (11th : 2014 : Beijing, China)
Contributor(s): Bonato, Anthony, 1971- [edt] | Chung, Fan R. K, 1949- [edt] | Prałat, Paweł [edt]
Material type: TextTextSeries: SerienbezeichnungLecture notes in computer science: 8882.; LNCS sublibrary: Publisher: Cham : Springer, [2014]Copyright date: ©2014Description: 1 online resource (ix, 161 pages) : illustrationsContent type: text, text Media type: computer, computer Carrier type: online resourceISBN: 9783319131238; 3319131230Other title: WAW 2014Subject(s): Computer algorithms -- Congresses | Data mining -- Congresses | World Wide Web -- Congresses | Computer algorithms | Data mining | World Wide WebGenre/Form: Conference papers and proceedings. Additional physical formats: Printed edition:: No titleDDC classification: 004.67/8 LOC classification: QA76.9.A43 | W425 2014Online resources: Click here to access online
Contents:
Intro; Preface; Organization; Contents; Clustering and the Hyperbolic Geometry of Complex Networks; 1 Introduction; 1.1 Random Geometric Graphs on the Hyperbolic Plane; 1.2 Notation; 2 Some Geometric Aspects of the Two Models; 3 The Clustering Coefficient; 4 Conclusions; References; Burning a Graph as a Model of Social Contagion; 1 Introduction; 2 Properties of the Burning Number; 2.1 Characterizations of Burning Number via Trees; 2.2 Bounds; 3 Burning in the ILT Model; 4 Cartesian Grids; 5 Conclusions and Future Work; References; Personalized PageRank with Node-Dependent Restart
1 Introduction and Definitions2 Occupation-Time Personalized PageRank; 3 Location-of-Restart Personalized PageRank; 4 Interesting Particular Cases; 4.1 Constant Probability of Restart; 4.2 Restart Probabilities Proportional to Powers of Degrees; 4.3 Random Walk with Jumps; 5 Discussion; References; Efficient Computation of the Weighted Clustering Coefficient; 1 Introduction; 1.1 Related Works; 2 Preliminaries; 2.1 Generalizations of Clustering Coefficient in Weighted Networks; 3 Computing the Weighted Clustering Coefficient in Probabilistic Networks
4 Efficient Estimators for the Weighted Clustering Coefficient5 Experiments; References; Global Clustering Coefficient in Scale-Free Networks; 1 Introduction; 2 Clustering Coefficients; 3 Scale-Free Graphs; 4 Existence of a Graph with Given Degree Distribution; 4.1 Result; 4.2 Auxiliary Results; 4.3 Proof of Theorem 1; 5 Global Clustering Coefficient; 5.1 Result; 5.2 Proof of Theorem 4; 6 Experiments; 7 Conclusion; References; Efficient Primal-Dual Graph Algorithms for MapReduce; 1 Introduction; 1.1 Problem Formulations and Results; 1.2 Technique: Width Modulation; 1.3 Related Work
2 Undirected Densest Subgraph2.1 Linear Program and Duality; 2.2 Width Modulation; 2.3 Binary Search for D*; 2.4 Rounding Step: Recovering the Densest Subgraph; 2.5 Summary of the Algorithm; 2.6 Number of MapReduce Phases; References; A The Multiplicative Weights Update Framework; B Densest Subgraph in Directed Graphs; B.1 Parametric LP Formulation; B.2 Covering Program and Width Modulation; B.3 Parametric Search; B.4 Rounding Step: Recovering the Densest Subgraph; C Fractional Matchings in Bipartite Graphs; C.1 Covering Program, Width Modulation, and Binary Search
C.2 Rounding Step: Recovering the Fractional MatchingReferences; Computing Diffusion State Distance Using Green's Function and Heat Kernel on Graphs; 1 Introduction; 2 Notation and Background; 3 Proof of Main Theorem; 4 Some Examples of the DSD Distance; 4.1 The Path Pn; 4.2 The Cycle Cn; 4.3 The Hypercube Qn; 5 Random Graphs; 6 Examples of Biological Networks; References; Relational Topic Factorization for Link Prediction in Document Networks; 1 Introduction; 2 Related Work; 3 Proposed Model; 3.1 Relational Topic Factorization; 3.2 Learning the Parameters; 4 Empirical Results; 4.1 Dataset
Summary: This book constitutes the refereed proceedings of the 11th International Workshop on Algorithms and Models for the Web Graph, WAW 2014, held in Beijing, China, in December 2014. The 12 papers presented were carefully reviewed and selected for inclusion in this volume. The aim of the workshop was to further the understanding of graphs that arise from the Web and various user activities on the Web, and stimulate the development of high-performance algorithms and applications that exploit these graphs. The workshop gathered the researchers who are working on graph-theoretic and algorithmic aspects of related complex networks, including social networks, citation networks, biological networks, molecular networks, and other networks arising from the Internet.
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This book constitutes the refereed proceedings of the 11th International Workshop on Algorithms and Models for the Web Graph, WAW 2014, held in Beijing, China, in December 2014. The 12 papers presented were carefully reviewed and selected for inclusion in this volume. The aim of the workshop was to further the understanding of graphs that arise from the Web and various user activities on the Web, and stimulate the development of high-performance algorithms and applications that exploit these graphs. The workshop gathered the researchers who are working on graph-theoretic and algorithmic aspects of related complex networks, including social networks, citation networks, biological networks, molecular networks, and other networks arising from the Internet.

Includes author index.

Intro; Preface; Organization; Contents; Clustering and the Hyperbolic Geometry of Complex Networks; 1 Introduction; 1.1 Random Geometric Graphs on the Hyperbolic Plane; 1.2 Notation; 2 Some Geometric Aspects of the Two Models; 3 The Clustering Coefficient; 4 Conclusions; References; Burning a Graph as a Model of Social Contagion; 1 Introduction; 2 Properties of the Burning Number; 2.1 Characterizations of Burning Number via Trees; 2.2 Bounds; 3 Burning in the ILT Model; 4 Cartesian Grids; 5 Conclusions and Future Work; References; Personalized PageRank with Node-Dependent Restart

1 Introduction and Definitions2 Occupation-Time Personalized PageRank; 3 Location-of-Restart Personalized PageRank; 4 Interesting Particular Cases; 4.1 Constant Probability of Restart; 4.2 Restart Probabilities Proportional to Powers of Degrees; 4.3 Random Walk with Jumps; 5 Discussion; References; Efficient Computation of the Weighted Clustering Coefficient; 1 Introduction; 1.1 Related Works; 2 Preliminaries; 2.1 Generalizations of Clustering Coefficient in Weighted Networks; 3 Computing the Weighted Clustering Coefficient in Probabilistic Networks

4 Efficient Estimators for the Weighted Clustering Coefficient5 Experiments; References; Global Clustering Coefficient in Scale-Free Networks; 1 Introduction; 2 Clustering Coefficients; 3 Scale-Free Graphs; 4 Existence of a Graph with Given Degree Distribution; 4.1 Result; 4.2 Auxiliary Results; 4.3 Proof of Theorem 1; 5 Global Clustering Coefficient; 5.1 Result; 5.2 Proof of Theorem 4; 6 Experiments; 7 Conclusion; References; Efficient Primal-Dual Graph Algorithms for MapReduce; 1 Introduction; 1.1 Problem Formulations and Results; 1.2 Technique: Width Modulation; 1.3 Related Work

2 Undirected Densest Subgraph2.1 Linear Program and Duality; 2.2 Width Modulation; 2.3 Binary Search for D*; 2.4 Rounding Step: Recovering the Densest Subgraph; 2.5 Summary of the Algorithm; 2.6 Number of MapReduce Phases; References; A The Multiplicative Weights Update Framework; B Densest Subgraph in Directed Graphs; B.1 Parametric LP Formulation; B.2 Covering Program and Width Modulation; B.3 Parametric Search; B.4 Rounding Step: Recovering the Densest Subgraph; C Fractional Matchings in Bipartite Graphs; C.1 Covering Program, Width Modulation, and Binary Search

C.2 Rounding Step: Recovering the Fractional MatchingReferences; Computing Diffusion State Distance Using Green's Function and Heat Kernel on Graphs; 1 Introduction; 2 Notation and Background; 3 Proof of Main Theorem; 4 Some Examples of the DSD Distance; 4.1 The Path Pn; 4.2 The Cycle Cn; 4.3 The Hypercube Qn; 5 Random Graphs; 6 Examples of Biological Networks; References; Relational Topic Factorization for Link Prediction in Document Networks; 1 Introduction; 2 Related Work; 3 Proposed Model; 3.1 Relational Topic Factorization; 3.2 Learning the Parameters; 4 Empirical Results; 4.1 Dataset

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