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.
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.
