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Full Description
This book consists of contributions from preeminent experts in the field of network science, signal processing and machine learning, focusing on the theoretical and algorithmic aspects of online social networking technologies. As online social networks provide an important and diverse medium for spreading and disseminating various types of information, this book offers new perspectives and applications of these large-scale networks in engineering cyber intelligence. The book introduces and explains how to design predictive analytics and computational tools, but also presents insights into forward-engineering new applications such as community detection, rumor source detection and large-scale online learning. Mathematical tools based on statistical inference, graph theory and machine learning as well as real-world data analysis are provided to help readers understand the advances in cyber intelligence. As such it is a valuable resource for graduate students and researchers in understanding the developments of online social networking technologies.
Contents
Preface; Generalized Modularity Embedding: A General Framework for Network Embedding; Epidemic Source Detection in Complex Networks; Subgraph Network for Expanding Structural Feature Space with Application to Graph Data Mining; Online Information Spreading and Source Identification; The Pagerank Bipartite Graph Ranking: Online Chat Group Recommendation; Parallel Counting of Subgraphs in Large Graphs: Pruning and Hierarchical Clustering Algorithms; Social Learning Network and Its Applications in Large Scale Online Education through Chatbot; Rumor Source Detection in Finite Graphs with Boundary Effects by Message-Passing Algorithms; Index.
