LEADER 00881nam a2200253 i 4500 001 991002149679707536 005 20020507160705.0 008 991209s1998 uk ||| | eng 020 $a019829378X 020 $a0198288514 035 $ab11615862-39ule_inst 035 $aLE02731334$9ExL 040 $aDip.to Studi Giuridici$bita 084 $aC-XX/A 100 1 $aHeller, Frank$0115618 245 10$aOrganizational participation :$bmyth and reality /$cFrank Heller... [et al] 260 $aOxford :$bOxford University Press,$c1998 300 $ax, 294 p. 907 $a.b11615862$b21-09-06$c02-07-02 912 $a991002149679707536 945 $aLE027 C-XX/A HEL01.01$g1$iLE027-6745$lle027$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i11831893$z02-07-02 996 $aOrganizational Participation$9693908 997 $aUNISALENTO 998 $ale027$b01-01-99$cm$da $e-$feng$guk $h0$i1 LEADER 06459nam 2200577 450 001 996534466403316 005 20230819122738.0 010 $a9783031322969$b(electronic bk.) 010 $z9783031322952 035 $a(MiAaPQ)EBC30545056 035 $a(Au-PeEL)EBL30545056 035 $a(OCoLC)1379802738 035 $a(BIP)090872633 035 $a(PPN)270612173 035 $a(EXLCZ)9926707052100041 100 $a20230819d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAlgorithms and Models for the Web Graph $e18th International Workshop, WAW 2023, Toronto, on, Canada, May 23-26, 2023, Proceedings /$fMegan Dewar [and four others], editors 205 $aFirst edition. 210 1$aCham, Switzerland :$cSpringer,$d[2023] 210 4$dİ2023 215 $a1 online resource (203 pages) 225 1 $aLecture Notes in Computer Science Series ;$vVolume 13894 311 08$aPrint version: Dewar, Megan Algorithms and Models for the Web Graph Cham : Springer,c2023 9783031322952 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Organization -- Contents -- Correcting for Granularity Bias in Modularity-Based Community Detection Methods -- 1 Introduction -- 2 Hyperspherical Geometry -- 3 The Heuristic -- 4 Derivation of the Heuristic -- 5 Experiments -- 6 Discussion -- References -- The Emergence of a Giant Component in One-Dimensional Inhomogeneous Networks with Long-Range Effects -- 1 Introduction and Statement of Result -- 1.1 The Weight-Dependent Random Connection Model -- 1.2 Main Result -- 1.3 Examples -- 2 Proof of the Main Theorem -- 2.1 Some Construction and Notation -- 2.2 Connecting Far Apart Vertex Sets -- 2.3 Existence of a Giant Component -- 2.4 Absence of an Infinite Component -- References -- Unsupervised Framework for Evaluating Structural Node Embeddings of Graphs -- 1 Introduction -- 2 Framework -- 2.1 Input/Output -- 2.2 Formal Description of the Algorithm -- 2.3 Properties -- 3 Experimentation -- 3.1 Synthetic Graphs Design -- 3.2 Algorithmic Properties of the Framework -- 3.3 Role Classification Case Study -- 4 Conclusion -- References -- Modularity Based Community Detection in Hypergraphs -- 1 Introduction -- 2 Modularity Functions -- 3 Hypergraph Modularity Optimization Algorithm -- 3.1 Louvain Algorithm -- 3.2 Challenges with Adjusting the Algorithm to Hypergraphs -- 3.3 Our Approach to Hypergraph Modularity Optimization: h-Louvain -- 4 Results -- 4.1 Synthetic Hypergraph Model: h-ABCD -- 4.2 Exhaustive Search for the Best Strategy -- 4.3 Comparing Basic Policies for Different Modularity Functions -- 5 Conclusions -- References -- Establishing Herd Immunity is Hard Even in Simple Geometric Networks -- 1 Introduction -- 2 Preliminaries -- 3 Unanimous Thresholds -- 4 Constant Thresholds -- 5 Majority Thresholds -- 6 Conclusions -- References -- Multilayer Hypergraph Clustering Using the Aggregate Similarity Matrix. 327 $a1 Introduction -- 2 Related Work -- 3 Algorithm and Main Results -- 4 Numerical Illustrations -- 5 Analysis of the Algorithm -- 5.1 SDP Analysis -- 5.2 Upper Bound on -- 5.3 Lower Bound on Dii -- 5.4 Assortativity -- 5.5 Proof of Theorem 1 -- 6 Conclusions -- References -- The Myth of the Robust-Yet-Fragile Nature of Scale-Free Networks: An Empirical Analysis -- 1 Introduction -- 2 Data -- 2.1 Network Collection -- 2.2 Network Categorization -- 2.3 Handling Weighted Networks -- 2.4 Preprocessing -- 3 Scale-Freeness Analysis -- 3.1 Scale-Freeness Classification Methods -- 3.2 Results -- 4 Robustness Analysis -- 4.1 Network Robustness -- 4.2 Configuration -- 4.3 Results -- 5 Conclusions -- 6 Appendix -- 6.1 Scale-Freeness Classification: Further Analysis -- 6.2 Robustness: Further Analysis -- 6.3 The Curious Case of Collins Yeast Interactome -- References -- A Random Graph Model for Clustering Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Homomorphism Counts in the Chung-Lu Model -- 4 Random Clustering Graph Model -- 5 Homomorphism Counts -- 5.1 Extension Configurations -- 5.2 Expected Homomorphism Counts -- 5.3 Concentration of Subgraph Counts -- References -- Topological Analysis of Temporal Hypergraphs -- 1 Introduction -- 2 Method and Background -- 2.1 Temporal Hypergraphs -- 2.2 Sliding Windows for Hypergraph Snapshots -- 2.3 Associated ASC of a Hypergraph -- 2.4 Simplicial Homology -- 2.5 Zigzag Persistent Homology -- 3 Applications -- 3.1 Social Network Analysis -- 3.2 Cyber Data Analysis -- 4 Conclusion -- References -- PageRank Nibble on the Sparse Directed Stochastic Block Model -- 1 Introduction -- 2 Main Results -- 3 Proofs -- 4 Results from Simulations -- 5 Remarks and Conclusions -- References -- A Simple Model of Influence -- 1 Introduction -- 2 Analysis for Random Graphs G(n,m) -- 3 Proof of Lemma 1. 327 $a4 The Effect of Stubborn Vertices -- 5 The Largest Fragment in G(n,m) -- References -- The Iterated Local Transitivity Model for Tournaments -- 1 Introduction -- 2 Small World Property -- 3 Motifs and Universality -- 4 Graph-Theoretic Properties of the Models -- 4.1 Hamiltonicity -- 4.2 Spectral Properties -- 4.3 Domination Numbers -- 5 Conclusion and Further Directions -- References -- Author Index. 330 8 $aThis book constitutes the proceedings of the 18th International Workshop on Algorithms and Models for the Web Graph, WAW 2023, held in Toronto, Canada, in May 23-26, 2023.The 12 Papers presented in this volume were carefully reviewed and selected from 21 submissions. The aim of the workshop was understanding of graphs that arise from the Web and various user activities on the Web, and stimulate the development of high-performance algorithms and applications that exploit these graphs. 410 0$aLecture notes in computer science ;$vVolume 13894. 606 $aComputer algorithms$vCongresses 606 $aData mining$vCongresses 606 $aWorld Wide Web$vCongresses 610 $aInformation Theory 610 $aComputer Science 610 $aComputers 615 0$aComputer algorithms 615 0$aData mining 615 0$aWorld Wide Web 676 $a005.1 702 $aDewar$b Megan 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a996534466403316 996 $aAlgorithms and Models for the Web-Graph$9772606 997 $aUNISA