Network Intelligence Meets User Centered Social Media Networks, Softcover reprint of the original 1st ed. 2018 Lecture Notes in Social Networks Series

This edited volume presents advances in modeling and computational analysis techniques related to networks and online communities. It contains the best papers of notable scientists from the 4th European Network Intelligence Conference (ENIC 2017) that have been peer reviewed and expanded into the present format. The aim of this text is to share knowledge and experience as well as to present recent advances in the field.  The book is a nice mix of basic research topics such as data-based centrality measures along with intriguing applied topics, for example, interaction decay patterns in online social communities. This book will appeal to students, professors, and researchers working in the fields of data science, computational social science, and social network analysis.  

Data-based centrality measures.- Extracting the Main Path of historic events from Wikipedia.- Simulating trade in economic networks with TrEcSim.- Community Aliveness: Discovering interaction decay patterns in online social communities.- Network Patterns of Direct and Indirect Reciprocity in edX MOOC Forums.- Targeting influential nodes for recovery in bootstrap percolation on hyperbolic networks.- Trump versus Clinton – Twitter communication during the US primaries.- Extended feature-driven graph model for Social Media Networks.- Market basket analysis using minimum spanning trees.- Behavior-based relevance estimation for social networks interaction relations.- Sponge walker: Community detection in large directed social networks using local structures and random walks.- Identifying promising research topics in Computer Science.- Identifying accelerators of information diffusion across social media channels .- Towards anILP approach for learning privacy heuristics from users' regrets.- Strength of nations: A case study on estimating the influence of leading countries using social media analysis.- Incremental learning in dynamic networks for node classification.

Reda Alhajj is a professor in the Department of Computer Science at the University of Calgary. He published over 450 papers in refereed international journals and conferences. He served on the program committee of several international conferences. He is founding editor in chief of the Springer premier journal “Social Networks Analysis and Mining”, founding editor-in-chief of Springer Series “Lecture Notes on Social Networks”, founding editor-in-chief of Springer journal “Network Modeling Analysis in Health Informatics and Bioinformatics”, founding co-editor-in-chief of Springer “Encyclopedia on Social Networks Analysis and Mining”, founding steering chair of the flagship conference “IEEE/ACM International Conference on Advances in Social Network Analysis and Mining”, and three accompanying symposiums FAB, FOSINT-SI and HI-BI-BI. Dr. Alhajj's research concentrates primarily on data science from management to integration and analysis. Current research efforts include: (1) data management and mining, (2) social network analysis with applications in sociology, computational biology and bioinformatics, homeland security, etc., (3) sequence analysis with emphasis on domains like financial, weather, traffic, energy, etc. Dr. Alhajj's is proud to have a number of successful teams, including SANO who ranked first in the Microsoft Imagine Cup Competition in Canada and received KFC Innovation Award in the World Finals held in Russia, TRAK who ranked in the top 15 teams in the open data analysis competition in Canada, Funiverse who ranked first in Microsoft Imagine Cup Competition in Canada.