Algorithms and Models for the Web Graph -

Algorithms and Models for the Web Graph

16th International Workshop, WAW 2019, Brisbane, QLD, Australia, July 6–7, 2019, Proceedings
Buch | Softcover
IX, 131 Seiten
2019 | 1st ed. 2019
Springer International Publishing (Verlag)
978-3-030-25069-0 (ISBN)
53,49 inkl. MwSt

This book constitutes the proceedings of the 16th International Workshop on Algorithms and Models for the Web Graph, WAW 2019, held in Brisbane, QLD, Australia, in July 2019.
The 9 full papers presented in this volume were carefully reviewed  and selected from 13 submissions. The papers cover topics of all aspects of algorithmic and mathematical research in the areas pertaining to the World Wide Web, espousing the view of complex data as networks.

Using Synthetic Networks for Parameter Tuning in Community Detection.- Efficiency of Transformations of Proximity Measures for Graph Clustering.- Almost Exact Recovery in Label Spreading.- Strongly n-e.c. Graphs and Independent Distinguishing Labellings.- The Robot Crawler Model on Complete k-Partite and Erdös-Rényi Random Graphs.- Estimating the Parameters of the Waxman Random Graph.- Understanding the Effectiveness of Data Reduction in Public Transportation Networks.- A Spatial Small-World Graph Arising from Activity-Based Reinforcement.- SimpleHypergraphs.jl - Novel Software Framework for Modelling and Analysis of Hypergraphs. 

Erscheinungsdatum
Reihe/Serie Lecture Notes in Computer Science
Theoretical Computer Science and General Issues
Zusatzinfo IX, 131 p. 24 illus., 14 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 231 g
Themenwelt Informatik Theorie / Studium Algorithmen
Mathematik / Informatik Mathematik Wahrscheinlichkeit / Kombinatorik
Schlagworte Algorithm analysis and problem complexity • algorithms • Clustering • clustering coefficient • community detection • complex networks • data structures • graph-based • graph clustering • Graphic methods • graph theory • Learning Algorithms • Modeling • Modularity • Random Graphs • Semi-Supervised Learning • supervised learning • Web graph
ISBN-10 3-030-25069-5 / 3030250695
ISBN-13 978-3-030-25069-0 / 9783030250690
Zustand Neuware
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