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 -- Efficient outlier detection in hyperedge streams using minHash and locality-sensitive hashing -- Layer-wise model stacking for link prediction in multilayer networks. Case of scientific 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.

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.