From Security to Community Detection in Social Networking Platforms 1st edition by Panagiotis Karampelas – Ebook PDF Instant Download/Delivery:3030112853,9783030112851
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ISBN 10:3030112853
ISBN 13:9783030112851
Author: Panagiotis Karampelas
This book focuses on novel and state-of-the-art scientific work in the area of detection and prediction techniques using information found generally in graphs and particularly in social networks. Community detection techniques are presented in diverse contexts and for different applications while prediction methods for structured and unstructured data are applied to a variety of fields such as financial systems, security forums, and social networks. The rest of the book focuses on graph-based techniques for data analysis such as graph clustering and edge sampling.
The research presented in this volume was selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM ’17). Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. This book will appeal to practitioners, researchers and students in the field.
From Security to Community Detection in Social Networking Platforms 1st Table of contents:
Real-World Application of Ego-Network Analysis to Evaluate Environmental Management Structures
An Evolutionary Approach for Detecting Communities in Social Networks
On Detecting Multidimensional Communities
Derivatives in Graph Space with Applications for Finding and Tracking Local Communities
Graph Clustering Based on Attribute-Aware Graph Embedding
On Counting Triangles Through Edge Sampling in Large Dynamic Graphs
Generation and Corruption of Semi-Structured and Structured Data
A Data Science Approach to Predict the Impact of Collateralization on Systemic Risk
Mining Actionable Information from Security Forums: The Case of Malicious IP Addresses
Temporal Methods to Detect Content-Based Anomalies in Social Media
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Panagiotis Karampelas,Community,Detection,Security