From Social Data Mining and Analysis to Prediction and Community Detection / edited by Mehmet Kaya, Özcan Erdoǧan, Jon Rokne.

Format:

Book

Contributor:

Kaya, Mehmet, editor.

Erdoğan, Özcan, 1974- editor.

Rokne, J. (Jon), 1941- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in social networks 2190-5428

Lecture Notes in Social Networks, 2190-5428

Language:

English

Subjects (All):

Data mining.

Artificial intelligence.

Physics.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Applications of Graph Theory and Complex Networks.

Local Subjects:

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Applications of Graph Theory and Complex Networks.

Physical Description:

1 online resource (X, 245 pages) : 78 illustrations, 53 illustrations in color.

Edition:

First edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

System Details:

text file PDF

Summary:

This book presents the state-of-the-art in various aspects of analysis and mining of online social networks. Within the broader context of online social networks, it focuses on important and upcoming topics of social network analysis and mining. The book collects chapters that are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'2015), which was held in Paris, France in August 2015. All papers have been peer reviewed and checked carefully for overlap with the literature. The book will appeal to students and researchers in social network analysis/mining and machine learning.

Contents:

Chapter1. An Offline-Online Visual Framework for Clustering Memes in Social Media

Chapter2. A System for Email Recipient Prediction

Chapter3. A Credibility Assessment Model for Online Social Network Content

Chapter4. Web Search Engine based Representation for Arabic Tweets Categorization

Chapter5. Sentiment Trends and Classifying Stocks using P-Trees

Chapter6. Mining Community Structure with Node Embeddings

Chapter7. A LexDFS-based Approach on finding compact communities

Chapter8. Computational Data Sciences and Regulation of Banking and Financial Services

Chapter9. Frequent and Non-Frequent Sequential Itemsets Detection.

Other Format:

Printed edition:

ISBN:

978-3-319-51367-6

9783319513676

Access Restriction:

Restricted for use by site license.