Coverart for item
The Resource Social Network Based Big Data Analysis and Applications, Mehmet Kaya, Jalal Kawash, Suheil Khoury, Min-Yuh Day, editors

Social Network Based Big Data Analysis and Applications, Mehmet Kaya, Jalal Kawash, Suheil Khoury, Min-Yuh Day, editors

Label
Social Network Based Big Data Analysis and Applications
Title
Social Network Based Big Data Analysis and Applications
Statement of responsibility
Mehmet Kaya, Jalal Kawash, Suheil Khoury, Min-Yuh Day, editors
Contributor
Editor
Subject
Language
eng
Summary
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
Member of
Cataloging source
N$T
Dewey number
302.231
Illustrations
illustrations
Index
index present
LC call number
HM742
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Kaya, Mehmet
  • Kawash, Jalal
  • Khoury, Suheil
  • Day, Min-Yuh
Series statement
Lecture notes in social networks
http://library.link/vocab/subjectName
  • Online social networks.
  • Big data.
Label
Social Network Based Big Data Analysis and Applications, Mehmet Kaya, Jalal Kawash, Suheil Khoury, Min-Yuh Day, editors
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Contents; Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons; 1 Introduction; 2 Related Work; 3 System Workflow; 3.1 Terms Extraction; 3.1.1 Term Extraction Evaluation; 3.2 Term Sentiment Evaluation; 3.2.1 Ensemble System Evaluation; 4 Lexicons Evaluation; 4.1 Dataset Description; 4.2 Evaluation on Nonfactual Texts; 5 Conclusion and Future Work; References; Hiding in Plain Sight: The Anatomy of Malicious Pages on Facebook; 1 Introduction; 2 Related Work; 3 Scope and Data Collection; 3.1 Scope; 3.2 Establishing Ground Truth; 3.3 Dataset
  • 4 Malicious Pages on Facebook4.1 Spatial Behavior; 4.1.1 Entities; 4.1.2 Content; 4.1.3 Network; 4.1.4 Metadata; 4.2 Temporal Behavior; 4.2.1 Posting Activity; 4.2.2 Attributes over Time; 5 Automatic Detection of Malicious Pages; 5.1 Supervised Learning with Page and Post Features; 5.2 Supervised Learning with Bag-of-Words; 6 Discussion and Limitations; 7 Conclusion and Future Work; References; Extraction and Analysis of Dynamic Conversational Networksfrom TV Series; 1 Introduction; 2 Previous Works; 2.1 Complete Aggregation; 2.2 Time-Slices; 3 Estimating Verbal Interactions
  • 3.1 Scene Co-occurrence3.2 Sequential Estimate of Verbal Interactions; 4 Dynamic Conversational Network for Plot Modeling; 4.1 Narrative Smoothing; 4.2 Narrative Smoothing Illustrated; 5 Experiments and Results; 5.1 Corpus; 5.2 Conversational Interactions; 5.3 Narrative Smoothing; 6 Conclusion and Perspectives; References; Diversity and Influence as Key Measures to Assess Candidates for Hiring or Promotion in Academia; 1 Introduction; 2 Related Work; 3 The Proposed Methodology; 3.1 The Data Set; 3.2 Data Retrieval; 3.3 Influence Values; 3.4 Community Detection
  • 4 Experiments, Results, and Discussion4.1 Influence; 4.2 Communities; 4.3 Limitations; 5 Conclusions and Future Research; References; Timelines of Prostate Cancer Biomarkers; 1 Introduction; 2 Related Work; 3 Approach; 3.1 Data Set; 3.2 Analysis; 3.2.1 Calculating Moving Linear Regression Angle Values; 3.2.2 Weighting the Years; 3.2.3 Calculating the Emerging/Demerging Scores; 3.2.4 Calculating the Memberships; 3.3 Web Portal; 4 Results and Discussion; 5 Conclusion and Future Work; References; Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks
  • 1 Introduction and Related Work2 Modified Influence Maximization Approach; 2.1 Formulation; 2.2 Algorithm for Mining Influential Nodes Under Intrinsic Activation; 2.3 Content Creators and Engagement in Online Social Networks; 2.4 Optimality of the Influence Maximization Algorithm with Intrinsic Activation; 3 Synthetic Experiments; 3.1 Small Organization Tree; 3.2 Larger Graphs and the Influence Function; 4 Experiments on a Real-World Twitter Dataset; 4.1 Data Collection; 4.2 Influence Spread Results; 5 A Centrality Metric Incorporating Intrinsic Activation; 6 Conclusions; References
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9783319781952
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-3-319-78196-9
Other physical details
illustrations.
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
Label
Social Network Based Big Data Analysis and Applications, Mehmet Kaya, Jalal Kawash, Suheil Khoury, Min-Yuh Day, editors
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Contents; Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons; 1 Introduction; 2 Related Work; 3 System Workflow; 3.1 Terms Extraction; 3.1.1 Term Extraction Evaluation; 3.2 Term Sentiment Evaluation; 3.2.1 Ensemble System Evaluation; 4 Lexicons Evaluation; 4.1 Dataset Description; 4.2 Evaluation on Nonfactual Texts; 5 Conclusion and Future Work; References; Hiding in Plain Sight: The Anatomy of Malicious Pages on Facebook; 1 Introduction; 2 Related Work; 3 Scope and Data Collection; 3.1 Scope; 3.2 Establishing Ground Truth; 3.3 Dataset
  • 4 Malicious Pages on Facebook4.1 Spatial Behavior; 4.1.1 Entities; 4.1.2 Content; 4.1.3 Network; 4.1.4 Metadata; 4.2 Temporal Behavior; 4.2.1 Posting Activity; 4.2.2 Attributes over Time; 5 Automatic Detection of Malicious Pages; 5.1 Supervised Learning with Page and Post Features; 5.2 Supervised Learning with Bag-of-Words; 6 Discussion and Limitations; 7 Conclusion and Future Work; References; Extraction and Analysis of Dynamic Conversational Networksfrom TV Series; 1 Introduction; 2 Previous Works; 2.1 Complete Aggregation; 2.2 Time-Slices; 3 Estimating Verbal Interactions
  • 3.1 Scene Co-occurrence3.2 Sequential Estimate of Verbal Interactions; 4 Dynamic Conversational Network for Plot Modeling; 4.1 Narrative Smoothing; 4.2 Narrative Smoothing Illustrated; 5 Experiments and Results; 5.1 Corpus; 5.2 Conversational Interactions; 5.3 Narrative Smoothing; 6 Conclusion and Perspectives; References; Diversity and Influence as Key Measures to Assess Candidates for Hiring or Promotion in Academia; 1 Introduction; 2 Related Work; 3 The Proposed Methodology; 3.1 The Data Set; 3.2 Data Retrieval; 3.3 Influence Values; 3.4 Community Detection
  • 4 Experiments, Results, and Discussion4.1 Influence; 4.2 Communities; 4.3 Limitations; 5 Conclusions and Future Research; References; Timelines of Prostate Cancer Biomarkers; 1 Introduction; 2 Related Work; 3 Approach; 3.1 Data Set; 3.2 Analysis; 3.2.1 Calculating Moving Linear Regression Angle Values; 3.2.2 Weighting the Years; 3.2.3 Calculating the Emerging/Demerging Scores; 3.2.4 Calculating the Memberships; 3.3 Web Portal; 4 Results and Discussion; 5 Conclusion and Future Work; References; Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks
  • 1 Introduction and Related Work2 Modified Influence Maximization Approach; 2.1 Formulation; 2.2 Algorithm for Mining Influential Nodes Under Intrinsic Activation; 2.3 Content Creators and Engagement in Online Social Networks; 2.4 Optimality of the Influence Maximization Algorithm with Intrinsic Activation; 3 Synthetic Experiments; 3.1 Small Organization Tree; 3.2 Larger Graphs and the Influence Function; 4 Experiments on a Real-World Twitter Dataset; 4.1 Data Collection; 4.2 Influence Spread Results; 5 A Centrality Metric Incorporating Intrinsic Activation; 6 Conclusions; References
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9783319781952
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-3-319-78196-9
Other physical details
illustrations.
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote

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