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Modelling and Mining Networks

20th International Workshop, WAW 2025, Vilnius, Lithuania, June 30 – July 3, 2025, Proceedings...

Mindaugas Bloznelis Paulius Drungilas Bogumił Kamiński Paweł Prałat

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English
Springer International Publishing AG
22 May 2025
This book constitutes the refereed proceedings of the 20th International Workshop on Modelling and Mining Networks, WAW 2025, held in Vilnius, Lithuania, during June 30–July 3, 2025. The 13 full papers presented in this book were carefully reviewed and selected from 20 submissions.

The aim of the 20th Workshop on Modelling and Mining Networks 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.
Edited by:   , , , ,
Imprint:   Springer International Publishing AG
Country of Publication:   Switzerland
Volume:   15699
Dimensions:   Height: 235mm,  Width: 155mm, 
ISBN:   9783031928970
ISBN 10:   3031928970
Series:   Lecture Notes in Computer Science
Pages:   205
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active
.- Linear Geometric Centralities. .- Analysis and predictability of centrality measures in competition networks. .- Modularity of Random Intersection Graphs. .- The size of the giant in inhomogeneous random graphs of preferential attachment type. .- k-connectivity threshold for superpositions of Bernoulli random graphs. .- Improving community detection via community association strength scores. .- PageRank under interpolation between undirected- and directed networks - a case study. .- Degrees in preferential attachment networks with an anomaly. .- The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities (ABCD+o2). .- A Graph Network Approach to Disinformation Detection in Social Media. .- Computation of the Laplacian Spectral Barycentre Network in a Soules Basis. .- The Multilayer Artificial Benchmark for Community Detection (mABCD). .- Integrating Link Prediction and Isolation Forest for Backbone Extraction.

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