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

Contributor(s):

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Lecture Notes in Social Networks | SpringerLink Bücher | Springer eBook Collection Computer SciencePublisher: Cham : Springer, 2017Description: Online-Ressource (X, 245 p. 78 illus., 53 illus. in color, online resource)ISBN:

9783319513676

Subject(s):

Additional physical formats: 9783319513669 | Druckausg.: 978-3-319-51366-9 | Printed edition: 9783319513669 LOC classification:

QA76.9.D343

DOI: DOI: 10.1007/978-3-319-51367-6Online resources:

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 learningSummary: 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 DetectionPPN: PPN: 1657565890Package identifier: Produktsigel: ZDB-2-SCS

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