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

Інтелектуальна відповідальність: Вид матеріалу: Текст Серія: Lecture Notes in Social NetworksПублікація: Cham : Springer International Publishing : Imprint: Springer, 2017Видання: 1st ed. 2017Опис: X, 245 p. 78 illus., 53 illus. in color. online resourceТип вмісту:
  • text
Тип засобу:
  • computer
Тип носія:
  • online resource
ISBN:
  • 9783319513676
Тематика(и): Додаткові фізичні формати: Printed edition:: Немає назви; Printed edition:: Немає назви; Printed edition:: Немає назвиДесяткова класифікація Дьюї:
  • 006.312 23
Класифікація Бібліотеки Конгресу:
  • QA76.9.D343
Електронне місцезнаходження та доступ:
Вміст:
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.
У: Springer eBooksЗведення: 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.
Тип одиниці: ЕКнига Списки з цим бібзаписом: Springer Ebooks (till 2020 - Open Access)+(2017 Network Access)) | Springer Ebooks (2017 Network Access))
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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.

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.

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Онлайн доступ з авторизацією на https://link.springer.com/

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