LEADER 08458nam 22008535 450 001 9910483535003321 005 20230329172337.0 010 $a3-319-13123-0 024 7 $a10.1007/978-3-319-13123-8 035 $a(CKB)3710000000306174 035 $a(SSID)ssj0001386326 035 $a(PQKBManifestationID)11817444 035 $a(PQKBTitleCode)TC0001386326 035 $a(PQKBWorkID)11351545 035 $a(PQKB)10847702 035 $a(DE-He213)978-3-319-13123-8 035 $a(MiAaPQ)EBC6302159 035 $a(MiAaPQ)EBC5595464 035 $a(Au-PeEL)EBL5595464 035 $a(OCoLC)897803040 035 $a(PPN)183094638 035 $a(EXLCZ)993710000000306174 100 $a20141112d2014 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAlgorithms and Models for the Web Graph $e11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings /$fedited by Anthony Bonato, Fan Chung Graham, Pawe? Pra?at 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (IX, 161 p. 30 illus.) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v8882 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-13122-2 320 $aIncludes bibliographical references and index. 327 $aIntro -- 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 Definitions -- 2 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 Coefficient -- 5 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. 327 $a2 Undirected Densest Subgraph -- 2.1 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 Matching -- References -- 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 -- 4.2 Evaluation Metrics -- 4.3 In-matrix Prediction -- 4.4 Out-of-Matrix Prediction -- 4.5 Relationship with Document Properties -- 4.6 Examining Topic Spaces -- 5 Conclusions and Future Work -- References -- Firefighting as a Game -- 1 Introduction -- 2 Game-Theoretical Definitions -- 3 The Firefighting Game -- 3.1 Utility Functions -- 3.2 Quality of Equilibria -- 3.3 Price of Anarchy for Trees -- 4 Coalitions -- 4.1 Price of Anarchy -- 4.2 Graphs with Constant Cut-Width -- 5 Conclusions -- References -- PageRank in Scale-Free Random Graphs -- 1 Introduction -- 2 Directed Random Graphs -- 3 PageRank Iterations in the DCM. 327 $a4 Main Result: Coupling with a Thorny Branching Tree -- 5 Numerical Results -- References -- Modelling of Trends in Twitter Using Retweet Graph Dynamics -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 4 Retweet Graphs -- 5 Model -- 5.1 Growth of the Graph -- 5.2 Component Size Distribution -- 5.3 Influence of q, p and -- 6 The Model in Practice -- 7 Conclusion and Discussion -- References -- LiveRank: How to Refresh Old Crawls -- 1 Introduction -- 2 Model -- 2.1 Performance Metric -- 2.2 PageRank -- 2.3 Static LiveRanks -- 2.4 Sample-Based LiveRanks -- 2.5 Dynamic LiveRanks -- 3 Datasets -- 3.1 uk-2002 Dataset -- 3.2 uk-2006 Dataset -- 3.3 Correlations -- 4 LiveRanks Evaluation -- 4.1 Static and Sample-Based LiveRanks -- 4.2 Quantitative and Qualitative Impact of the Training Set -- 4.3 Dynamic LiveRanks -- 4.4 uk-2006 Dataset -- 4.5 Comparison with a Site-Based Approach -- 5 Conclusion -- References -- Author Index. 330 $aThis book constitutes the refereed proceedings of the 11th International Workshop on Algorithms and Models for the Web Graph, WAW 2014, held in Beijing, China, in December 2014. The 12 papers presented were carefully reviewed and selected for inclusion in this volume. The aim of the workshop was to further the understanding of graphs that arise from the Web and various user activities on the Web, and stimulate the development of high-performance algorithms and applications that exploit these graphs. The workshop gathered the researchers who are working on graph-theoretic and algorithmic aspects of related complex networks, including social networks, citation networks, biological networks, molecular networks, and other networks arising from the Internet. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v8882 606 $aAlgorithms 606 $aComputer science?Mathematics 606 $aDiscrete mathematics 606 $aData mining 606 $aInformation storage and retrieval systems 606 $aApplication software 606 $aAlgorithms 606 $aDiscrete Mathematics in Computer Science 606 $aData Mining and Knowledge Discovery 606 $aInformation Storage and Retrieval 606 $aComputer and Information Systems Applications 615 0$aAlgorithms. 615 0$aComputer science?Mathematics. 615 0$aDiscrete mathematics. 615 0$aData mining. 615 0$aInformation storage and retrieval systems. 615 0$aApplication software. 615 14$aAlgorithms. 615 24$aDiscrete Mathematics in Computer Science. 615 24$aData Mining and Knowledge Discovery. 615 24$aInformation Storage and Retrieval. 615 24$aComputer and Information Systems Applications. 676 $a005.1 702 $aBonato$b Anthony$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGraham$b Fan Chung$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPra?at$b Pawe?$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483535003321 996 $aAlgorithms and Models for the Web-Graph$9772606 997 $aUNINA