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Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining

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Cover of 'Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Intent Mining for the Good, Bad, and Ugly Use of Social Web: Concepts, Methods, and Challenges
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    Chapter 2 Bot-ivistm: Assessing Information Manipulation in Social Media Using Network Analytics
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    Chapter 3 Studying Fake News via Network Analysis: Detection and Mitigation
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    Chapter 4 Predictive Analysis on Twitter: Techniques and Applications
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    Chapter 5 Using Subgraph Distributions for Characterizing Networks and Fitting Random Graph Models
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    Chapter 6 Testing Assessment of Group Collaborations in OSNs
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    Chapter 7 Dynamics of Overlapping Community Structures with Application to Expert Identification
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    Chapter 8 On Dynamic Topic Models for Mining Social Media
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    Chapter 9 Domain-Specific Use Cases for Knowledge-Enabled Social Media Analysis
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    Chapter 10 Privacy in Human Computation: User Awareness Study, Implications for Existing Platforms, Recommendations, and Research Directions
Attention for Chapter 5: Using Subgraph Distributions for Characterizing Networks and Fitting Random Graph Models
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Citations

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Chapter title
Using Subgraph Distributions for Characterizing Networks and Fitting Random Graph Models
Chapter number 5
Book title
Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining
Published by
Springer, Cham, September 2018
DOI 10.1007/978-3-319-94105-9_5
Book ISBNs
978-3-31-994104-2, 978-3-31-994105-9
Authors

Benjamin Cabrera, Cabrera, Benjamin

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 100%
Readers by discipline Count As %
Social Sciences 1 100%

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