Social network based big data analysis and applications
Author(s)
Bibliographic Information
Social network based big data analysis and applications
（Lecture notes in social networks）
Springer, c2018
- : [hardback]
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring practices, and subscription-type prediction in mobile phone services.
Manuscripts are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'2016), which was held in August 2016.
The papers were among the best featured at the meeting and were then improved and extended substantially. Social Network Based Big Data Analysis and Applications will appeal to students and researchers in the field.
Table of Contents
Chapter1. Twitter as a Source for Time and Domain Dependent Sentiment Lexicons.- Chapter2. Hiding in Plain Sight: The Anatomy of Malicious Pages on Facebook.- Chapter3. Extraction and Analysis of Dynamic Conversational Networks from TV Series.- Chapter4. Diversity and Influence as Key Measures to Assess Candidates for Hiring or Promotion in Academia.- Chapter5. Timelines of Prostate Cancer Biomarkers.- Chapter6. Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks.- Chapter7. Influence and Extension of the Spiral of Silence in Social Networks: A Data-driven Approach.- Chapter8. Prepaid or Postpaid? That is the question.\\ Novel Methods of Subscription Type Prediction in Mobile Phone Services.- Chapter9. Dynamic Pattern Detection for Big Data Stream Analytics.- Chapter10. Community-based Recommendation for Cold-Start Problem: A Case Study of Reciprocal Online Dating Recommendation.- Chapter11. Combining Feature Extraction and Clustering for Better Face Recognition.
by "Nielsen BookData"
