TY  - BOOK
AU  - Dewar,Megan
AU  - Kamiński,Bogumił
AU  - Kaszyński,Daniel
AU  - Kraiński,Łukasz
AU  - Prałat,Paweł
AU  - Théberge,François
AU  - Wrzosek,Małgorzata
ED  - SpringerLink (Online service)
TI  - Modelling and Mining Networks: 19th International Workshop, WAW 2024, Warsaw, Poland, June 3–6, 2024, Proceedings 
T2  - Lecture Notes in Computer Science,
SN  - 9783031592058
AV  - QA75.5-76.95 
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Computer science
KW  - Data structures (Computer science)
KW  - Information theory
KW  - Application software
KW  - Mathematics
KW  - Discrete mathematics
KW  - Theory of Computation
KW  - Data Structures and Information Theory
KW  - Computer and Information Systems Applications
KW  - Discrete Mathematics in Computer Science
N1  -  -- Subgraph Counts in Random Clustering Graphs -- Self similarity of Communities of the ABCD Model -- A simple model of influence Details and variants of dynamics -- Impact of Market Design and Trading Network Structure on Market Efficiency -- Network Embedding Exploration Tool (NEExT) -- Efficient Computation of k Edge Connected Components: An Empirical Analysis -- The directed Age dependent Random Connection Model with arc reciprocity -- How to cool a graph -- Distributed averaging for accuracy prediction in networked systems -- Towards Graph Clustering for Distributed Computing Environments -- Hypergraph Repository A Community driven and Interactive Hypernetwork Data Collection -- Clique Counts for Network Similarity
N2  - This book constitutes the refereed proceedings of the 19th International Workshop on Modelling and Mining Networks, WAW 2024, held in Warsaw, Poland, during June 3–6, 2024. The 12 full papers presented in this book were carefully reviewed and selected from 19 submissions. The aim of this workshop was to further the understanding of networks that arise in theoretical as well as applied domains. The goal was also to stimulate the development of high-performance and scalable algorithms that exploit these networks.
UR  - https://doi.org/10.1007/978-3-031-59205-8
ER  -