Complex Networks & Their Applications VI

Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications)
1. Auflage 2018
ISBN: 978-3-319-72149-1
Verlag: Springer International Publishing

Buch, Englisch, Band 689, 1288 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 2318 g

Reihe: Studies in Computational Intelligence

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the VI International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2017), which took place in Lyon on November 29 – December 1, 2017. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, network dynamics; diffusion, epidemics and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and ecological networks and technological networks.

Zielgruppe

Research

Autoren/Hrsg.

Cherifi, Chantal

Musolesi, Mirco

Cherifi, Hocine

Karsai, Márton

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Part I: Network measures.- A comparison of approaches to computing betweenness centrality for large graphs.- Cycle-centrality in economic and biological networks.- A game theoretic neighbourhood-based relevance index.- The impact of partially missing communities on the reliability of centrality measures.- Consistent estimation of mixed memberships with successive projections.- Reducing pivots of approximated betweenness computation by hierarchically clustering complex networks.- Power network equivalents: a network science based k-means clustering method integrated with silhouette analysis.- Part II: Link Analysis and Ranking.- Newton’s gravitational law for link prediction in social networks.- Ef?cient outlier detection in hyperedge streams using minHash and locality-sensitive hashing.- Layer-wise model stacking for link prediction in multilayer networks. Case of scienti?c collaboration networks.- Evolutionary community mining for link prediction in dynamic networks.- Rank aggregation for course sequence discovery.- Part III: Community Structure.- Community-based feature selection for credit card default prediction.- Tracking bitcoin users activity using community detection on a network of weak signals.

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