Trends in social network analysis : information propagation, user behavior modeling, forecasting, and vulnerability assessment /
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| Imprint: | Cham, Switzerland : Springer, 2017. |
|---|---|
| Description: | 1 online resource (xiii, 255 pages) : illustrations (some color) |
| Language: | English |
| Series: | Lecture notes in social networks Lecture notes in social networks. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11273551 |
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| 245 | 0 | 0 | |a Trends in social network analysis : |b information propagation, user behavior modeling, forecasting, and vulnerability assessment / |c Rokia Missaoui, Talel Abdessalem, Matthieu Latapy, editors. |
| 264 | 1 | |a Cham, Switzerland : |b Springer, |c 2017. | |
| 300 | |a 1 online resource (xiii, 255 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Lecture notes in social networks | |
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed May 4, 2017). | |
| 505 | 0 | |a Preface; Contents; Contributors; The Perceived Assortativity of Social Networks: Methodological Problems and Solutions; 1 Introduction; 2 Assortativity in Social and Other Networks; 2.1 Literature Search: Method; 2.2 Literature Search: Results; 2.3 Literature Search: Conclusions; 3 Methodological Pitfalls and False Assortativity; 3.1 Group-Based Networks and Assortativity; 3.2 Modeling Group-Based Sampling; 3.3 Filtering Networks; 4 Solutions; 4.1 Increased Sampling; 4.2 Use of Null Models; 4.3 Analysing Weighted Networks; 4.4 Using Diadic Over Group-Based Approaches; 4.5 Modern Technology. | |
| 505 | 8 | |a 4.6 Alternatives to the Newman Degree Correlation Measure5 Conclusions; References; A Parametric Study to Construct Time-Aware Social Profiles; 1 Introduction; 2 Related Works; 2.1 User Profile Building Process; 2.2 Incorporating Dynamic Interests in the Profile; 2.3 Social Network Evolution; 3 Proposition: Temporal Scores to Construct Social Profiles; 3.1 Notations; 3.2 *-0.9pc; 3.3 Community-Based Social Profile Construction Process with Temporal Score; 3.3.1 Temporal Score Calculation; 3.3.2 Temporal Score Integration; 4 Experiments; 4.1 Dataset Description. | |
| 505 | 8 | |a 4.2 Analysis of Common Keywords Between DBLP and Mendeley4.3 Case Study; 4.3.1 Ground Truth: Extraction of the Real User Profile from Mendeley; 4.3.2 Social Profiles Construction and Parametric Study; 4.4 Results; 4.4.1 All Users Results; 4.4.2 Results for Selected Users; 4.4.3 Different Time Decay Rate for the Relationships and the Information; 4.4.4 Discussion; 5 Conclusion and Future Works; Appendix; References; Sarcasm Analysis on Twitter Data Using Machine LearningApproaches; 1 Introduction; 2 Related Work; 2.1 Machine Learning-Based Approach; 2.2 Corpus-Based; 2.3 Lexical Features. | |
| 505 | 8 | |a 2.4 Pragmatic Feature2.5 Hyperbolic Feature; 3 Preliminaries; 3.1 System Model; 3.2 Part-of-Speech (POS) Tagging; 3.3 Parse Tree Generation; 4 Data Collection and Preprocessing; 4.1 Data Collection; 4.2 Preprocessing; 5 Proposed Scheme; 5.1 PBLGA; 5.2 LDC; 5.3 TCUF; 5.4 TCTDP; 6 Classifiers; 7 Results and Discussion; 7.1 Experimental Results; 8 Conclusion; References; The DEvOTION Algorithm for Delurking in Social Networks; 1 Introduction; 2 Targeted Influence Maximization; 3 Delurking-Oriented Targeted Influence Maximization; 3.1 Problem Statement; 3.2 Identifying and Characterizing Lurkers. | |
| 505 | 8 | |a 3.3 Choosing the Information Diffusion Model3.4 Properties of the Proposed Objective Function; 3.5 Modeling the Information Diffusion Graph; 3.6 The DEvOTION Algorithm; 4 Experimental Evaluation; 4.1 Evaluation Methodology; 4.2 Experimental Setting; 4.3 Data; 5 Results; 5.1 Impact of Parameters in DEvOTION; 5.2 Comparison with Baselines; 5.3 Comparison with Influence Maximization Algorithms; 5.4 Comparison with KB-TIM; 5.5 Seed Characteristics; 5.6 Discussion; 6 Conclusions and Future Work; References; Social Engineering Threat Assessment Using a Multi-Layered Graph-Based Model. | |
| 520 | |a The book collects contributions from experts worldwide addressing recent scholarship in social network analysis such as influence spread, link prediction, dynamic network biclustering, and delurking. It covers both new topics and new solutions to known problems. The contributions rely on established methods and techniques in graph theory, machine learning, stochastic modelling, user behavior analysis and natural language processing, just to name a few. This text provides an understanding of using such methods and techniques in order to manage practical problems and situations. Trends in Social Network Analysis: Information Propagation, User Behavior Modelling, Forecasting, and Vulnerability Assessment appeals to students, researchers, and professionals working in the field. | ||
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 0 | |a Online social networks. |0 http://id.loc.gov/authorities/subjects/sh2006006990 | |
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| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
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| 650 | 7 | |a Data mining. |2 bicssc | |
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| 650 | 7 | |a Online social networks. |2 fast |0 (OCoLC)fst01741311 | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Missaoui, R., |e editor. |0 http://id.loc.gov/authorities/names/n95062821 | |
| 700 | 1 | |a Abdessalem, Talel, |e editor. | |
| 700 | 1 | |a Latapy, Matthieu, |e editor. | |
| 773 | 0 | |t Springer eBooks | |
| 776 | 0 | 8 | |i Print version: |t Trends in social network analysis. |d Cham, Switzerland : Springer, 2017 |z 331953419X |z 9783319534190 |w (OCoLC)968507637 |
| 830 | 0 | |a Lecture notes in social networks. | |
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