DOCSLIB.ORG

Social Network Dynamics

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Social Network Dynamics Universita` degli Studi di Pisa Dipartimento di Informatica Dottorato di Ricerca in Informatica Settore Scientifico Disciplinare: INF/01 Ph.D. Thesis Social Network Dynamics Giulio Rossetti Supervisor Supervisor Dino Pedreschi Fosca Giannotti University of Pisa ISTI-CNR May 5, 2015 Abstract This thesis focuses on the analysis of structural and topological network problems. In particular, in this work the privileged subjects of investigation will be both static and dynamic social networks. Nowadays, the constantly growing availability of Big Data describing human behaviors (i.e., the ones provided by online social networks, telco companies, insurances, airline companies. ) of- fers the chance to evaluate and validate, on large scale realities, the performances of algorithmic approaches and the soundness of sociological theories. In this scenario, exploiting data-driven methodologies enables for a more careful modeling and thorough understanding of observed phe- nomena. In the last decade, graph theory has lived a second youth: the scientific community has extensively adopted, and sharpened, its tools to shape the so called Network Science. Within this highly active field of research, it is recently emerged the need to extend classic network analytical methodologies in order to cope with a very important, previously underestimated, semantic infor- mation: time. Such awareness has been the linchpin for recent works that have started to redefine form scratch well known network problems in order to better understand the evolving nature of human interactions. Indeed, social networks are highly dynamic realities: nodes and edges appear and disappear as time goes by describing the natural lives of social ties: for this reason. it is mandatory to assess the impact that time-aware approaches have on the solution of network prob- lems. Moving from the analysis of the strength of social ties, passing through node ranking and link prediction till reaching community discovery, this thesis aims to discuss data-driven methodologies specifically tailored to approach social network issues in semantic enriched scenarios. To this end, both static and dynamic analytical processes will be introduced and tested on real world data. 4 5 The only reason for time is so that everything doesn't happen at once. | Albert Einstein 6 Contents 1 Introduction 15 I Setting the Stage 21 2 Network Analysis 23 2.1 The Graph Representation . 24 2.2 Network Properties . 26 2.3 Network Models . 29 2.3.1 Random Graphs . 29 2.3.2 Small World . 29 2.3.3 Scale Free . 31 2.3.4 Forest Fire . 31 3 Complex Networks and Time 33 3.1 Local Structures . 36 3.2 Topologies . 40 3.3 Diffusion of Information . 42 4 Related Works 43 4.1 Local Structures: Individual Entities analysis . 44 4.1.1 Tie Strength . 44 4.1.2 Link Prediction . 44 4.1.3 Link-Based Object Ranking . 46 4.1.4 Multiplex Networks . 46 4.2 Topologies: Collective analysis . 48 4.2.1 Community Discovery . 48 4.2.2 Network Quantification . 50 4.2.3 Social Engagement . 51 4.3 Diffusion of Information . 52 4.4 Temporal Networks . 53 5 Social Network Data 55 5.1 Social Network Analysis . 56 5.1.1 Social Networks . 57 5.1.2 Collaboration Networks . 57 5.2 Static vs. Dynamic Social Networks . 58 II Frozen in Time: Portrait of a Social Network 59 6 Understanding Local Structures 61 6.1 Multidimensional Networks . 62 8 CHAPTER 0. CONTENTS 6.1.1 Multidimensional network measures . 62 6.2 Ties Strength . 66 6.2.1 Multidimensional Formulation . 66 6.3 Link-Based Object Ranking . 71 6.3.1 Network-Based Human Resources . 72 6.3.2 The Ubik Algorithm . 74 7 Understanding Topologies 83 7.1 The Modular Organization of a Network . 84 7.1.1 The Demon Algorithm . 86 7.1.2 The Overlap in Social Networks . 99 7.2 Homophily and Quantification . 104 7.2.1 Community Discovery for Quantification . 106 7.2.2 Ego-networks for Quantification . 107 7.3 Social Engagement: Skype . 114 7.3.1 Understanding Group Engagement . 115 III Social Dynamics: Networks through Time 127 8 Modeling Individual Dynamics 129 8.1 Unsupervised Link Prediction . 130 8.1.1 Multidimensional Link Prediction . 130 8.2 Superivsed Interaction Prediction . 140 8.2.1 Time-Aware Interaction Prediction . 141 9 Modeling Collective Dynamics 155 9.1 Evolutionary Community Discovery . 156 9.1.1 The Tiles algorithm . 158 10 Information Diffusion 171 10.1 Social Prominence . 172 10.1.1 Leader Characterization . 172 10.1.2 Local Diffusion Measures . 175 11 Conclusion 185 Bibliography 187 Index 199 List of Figures 1.1 From static to dynamic: Individual and Collective analysis . 17 2.1 Graph Topologies . 24 2.2 Graph Properties: Toy example . 26 2.3 Small World connectivity . 30 2.4 Scale Free network growth . 31 3.1 Dynamic network problems and their classification . 34 3.2 Graphical representation of tie strength in a social context. 36 3.3 Link Prediction: an example. 37 3.4 Multidimensional Network: An example . 38 5.1 From Static to Dynamic: Network representations . 58 6.1 Multidimensional Measures: Toy example . 62 6.2 Ties Strength: Local and global visualization . 67 6.3 Ties Strength: 4-dimensional network details . 68 6.4 Ties Strength: Network resilience . 69 6.5 Ties Strength: Measure correlations . 69 6.6 Ubik: Toy example . 73 6.7 Ubik: Running times . 76 6.8 Ubik: q-q plots against degree centrality ranking . 77 7.1 Demon: Intuition example . 84 7.2 Demon: Label Propagation example . 88 7.3 Demon: F-measure in benchmark networks . 93 7.4 Demon: impact . 96 7.5 Demon: Distribution of community sizes . 96 7.6 Demon: Amazon example . 97 7.7 Demon: Overlap in multidimensional network . 100 7.8 Demon: Multidimensional overlap in Facebook . 101 7.9 Quantification: Quantification vs Classification . 105 7.10 Quantification: Community-based approach . 106 7.11 Quantification: 1-hop and 2-hop ego-networks . 107 7.12 Quantification: Ego-network based approach . 108 7.13 Quantification: Label frequencies . 109 7.14 Quantification: Label ferquency distribution . 110 7.15 Group Engagement: From local to global . 115 7.16 Group Engagement: Approaches characterization . 116 7.17 Distribution of community size for HDemon, Louvain, Ego-Networks and BFS. 119 7.18 Group Engagements: SGD weigths in balanced scenario . 121 7.19 Group Engagements: AUC study in balanced scenario . 122 7.20 Group Engagements: Lift charts unbalanced scenario . 122 10 CHAPTER 0. LIST OF FIGURES 7.21 Group Engagements: SGD weights in unbalanced scenario . 124 7.22 Group Engagements: AUC study in unbalanced scenario . 124 7.23 Group Engagements: Pearson Correlation . 125 8.1 Link Prediction: Toy example . 132 8.2 Link Prediction: ROC curves for Neighbors models . 136 8.3 Link Prediction: ROC curves for NeighborsXOR models . 137 8.4 Link Prediction: Running times . 138 8.5 Supervised Link Prediction: Moving Average AUC . 147 8.6 Supervised Link Prediction: ROC curves balanced scenario . 148 8.7 Supervised Link Prediction: Feature relevance . 149 8.8 Supervised Link Prediction: Squared errors per feature . 152 8.9.
Load more

Recommended publications
  • Opinion-Based Centrality in Multiplex Networks: a Convex Optimization Approach
    Opinion-Based Centrality in Multiplex Networks: A Convex Optimization Approach Alexandre Reiffers-Masson and Vincent Labatut Laboratoire Informatique d'Avignon (LIA) EA 4128 Universit´ed'Avignon, France June 12, 2017 Abstract Most people simultaneously belong to several distinct social networks, in which their relations can be different. They have opinions about certain topics, which they share and spread on these networks, and are influenced by the opinions of other persons. In this paper, we build upon this observation to propose a new nodal centrality measure for multiplex networks. Our measure, called Opinion centrality, is based on a stochastic model representing opinion propagation dynamics in such a network. We formulate an optimization problem consisting in maximizing the opinion of the whole network when controlling an external influence able to affect each node individually. We find a mathematical closed form of this problem, and use its solution to derive our centrality measure. According to the opinion centrality, the more a node is worth investing external influence, and the more it is central. We perform an empirical study of the proposed centrality over a toy network, as well as a collection of real-world networks. Our measure is generally negatively correlated with existing multiplex centrality measures, and highlights different types of nodes, accordingly to its definition. Cite as: A. Reiffers-Masson & V. Labatut. Opinion-based Centrality in Multiplex Networks. Network Science, 5(2):213-234, 2017. Doi: 10.1017/nws.2017.7 1 Introduction In our ultra-connected world, many people simultaneously belong to several distinct social networks, in which their relations can be different.
    [Show full text]
  • Syllabus Organizational Network Analysis V2.2
    SYLLABUS Organizational Network Analysis Self-paced online Learn to set up an organizational network analysis and create masterclass an x-ray vision into the inner workings of your organization. INDEX About the AIHR Academy Page 3 Masterclass overview Page 4 Learning objectives Page 6 Who you will learn from Page 7 What you will learn Page 8 Your success team Page 13 Frequently Asked Questions Page 14 Enroll Now Page 16 Organizational Network Analysis | Syllabus Copyright © HR Analytics Academy | Page 2 ABOUT THE AIHR ACADEMY With the HR Analytics Academy we recorded so that you can learn whenever and teach the skills that you need in order wherever it’s most convenient. By placing to succeed in the field of People you in the driver’s seat we enable you to optimize your own learning curve and Analytics. By teaching you to leverage complete each course at your own pace. the power of data, we enable you to claim the strategic impact that you Practical bite-sized lessons deserve. The practical nature of our courses and the People Analytics is about leveraging data in way they are structured is what sets us apart. order to make better informed (data-driven) Our lessons are bite-sized and conveniently people decisions. Decisions which in the end split up into several modules. Within each drive better outcomes for both the business module you will typically find a combination and employees. of 3 – 4 video lessons, a short quiz, reading materials that provide extra context, a piece Online learning portal of bonus content, and a practical assignment All of our courses and masterclasses are that will help you put your new skills into delivered through the online learning portal.
    [Show full text]
  • Digital Platform As a Double-Edged Sword: How to Interpret Cultural Flows in the Platform Era
    International Journal of Communication 11(2017), 3880–3898 1932–8036/20170005 Digital Platform as a Double-Edged Sword: How to Interpret Cultural Flows in the Platform Era DAL YONG JIN Simon Fraser University, Canada This article critically examines the main characteristics of cultural flows in the era of digital platforms. By focusing on the increasing role of digital platforms during the Korean Wave (referring to the rapid growth of local popular culture and its global penetration starting in the late 1990s), it first analyzes whether digital platforms as new outlets for popular culture have changed traditional notions of cultural flows—the forms of the export and import of popular culture mainly from Western countries to non-Western countries. Second, it maps out whether platform-driven cultural flows have resolved existing global imbalances in cultural flows. Third, it analyzes whether digital platforms themselves have intensified disparities between Western and non- Western countries. In other words, it interprets whether digital platforms have deepened asymmetrical power relations between a few Western countries (in particular, the United States) and non-Western countries. Keywords: digital platforms, cultural flows, globalization, social media, asymmetrical power relations Cultural flows have been some of the most significant issues in globalization and media studies since the early 20th century. From television programs to films, and from popular music to video games, cultural flows as a form of the export and import of cultural materials have been increasing. Global fans of popular culture used to enjoy films, television programs, and music by either purchasing DVDs and CDs or watching them on traditional media, including television and on the big screen.
    [Show full text]
  • Modeling Customer Preferences Using Multidimensional Network Analysis in Engineering Design
    Modeling customer preferences using multidimensional network analysis in engineering design Mingxian Wang1, Wei Chen1, Yun Huang2, Noshir S. Contractor2 and Yan Fu3 1 Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA 2 Science of Networks in Communities, Northwestern University, Evanston, IL 60208, USA 3 Global Data Insight and Analytics, Ford Motor Company, Dearborn, MI 48121, USA Abstract Motivated by overcoming the existing utility-based choice modeling approaches, we present a novel conceptual framework of multidimensional network analysis (MNA) for modeling customer preferences in supporting design decisions. In the proposed multidimensional customer–product network (MCPN), customer–product interactions are viewed as a socio-technical system where separate entities of `customers' and `products' are simultaneously modeled as two layers of a network, and multiple types of relations, such as consideration and purchase, product associations, and customer social interactions, are considered. We first introduce a unidimensional network where aggregated customer preferences and product similarities are analyzed to inform designers about the implied product competitions and market segments. We then extend the network to a multidimensional structure where customer social interactions are introduced for evaluating social influence on heterogeneous product preferences. Beyond the traditional descriptive analysis used in network analysis, we employ the exponential random graph model (ERGM) as a unified statistical
    [Show full text]
  • Dynamic Social Network Analysis: Present Roots and Future Fruits
    Dynamic Social Network Analysis: Present Roots and Future Fruits Ms. Nancy K Hayden Project Leader Defense Threat Reduction Agency Advanced Systems and Concepts Office Stephen P. Borgatti, Ronald L. Breiger, Peter Brooks, George B. Davis, David S. Dornisch, Jeffrey Johnson, Mark Mizruchi, Elizabeth Warner July 2009 DEFENSE THREAT REDUCTION AGENCY •ADVANCED SYSTEMS AND CONCEPTS OFFICE REPORT NUMBER ASCO 2009 009 The mission of the Defense Threat Reduction Agency (DTRA) is to safeguard America and its allies from weapons of mass destruction (chemical, biological, radiological, nuclear, and high explosives) by providing capabilities to reduce, eliminate, and counter the threat, and mitigate its effects. The Advanced Systems and Concepts Office (ASCO) supports this mission by providing long-term rolling horizon perspectives to help DTRA leadership identify, plan, and persuasively communicate what is needed in the near term to achieve the longer-term goals inherent in the agency’s mission. ASCO also emphasizes the identification, integration, and further development of leading strategic thinking and analysis on the most intractable problems related to combating weapons of mass destruction. For further information on this project, or on ASCO’s broader research program, please contact: Defense Threat Reduction Agency Advanced Systems and Concepts Office 8725 John J. Kingman Road Ft. Belvoir, VA 22060-6201 [email protected] Or, visit our website: http://www.dtra.mil/asco/ascoweb/index.htm Dynamic Social Network Analysis: Present Roots and Future Fruits Ms. Nancy K. Hayden Project Leader Defense Threat Reduction Agency Advanced Systems and Concepts Office and Stephen P. Borgatti, Ronald L. Breiger, Peter Brooks, George B. Davis, David S.
    [Show full text]
  • Multilayer Networks
    Journal of Complex Networks (2014) 2, 203–271 doi:10.1093/comnet/cnu016 Advance Access publication on 14 July 2014 Multilayer networks Mikko Kivelä Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK Alex Arenas Departament d’Enginyeria Informática i Matemátiques, Universitat Rovira I Virgili, 43007 Tarragona, Spain Marc Barthelemy Downloaded from Institut de Physique Théorique, CEA, CNRS-URA 2306, F-91191, Gif-sur-Yvette, France and Centre d’Analyse et de Mathématiques Sociales, EHESS, 190-198 avenue de France, 75244 Paris, France James P. Gleeson MACSI, Department of Mathematics & Statistics, University of Limerick, Limerick, Ireland http://comnet.oxfordjournals.org/ Yamir Moreno Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza 50018, Spain and Department of Theoretical Physics, University of Zaragoza, Zaragoza 50009, Spain and Mason A. Porter† Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, by guest on August 21, 2014 Oxford OX2 6GG, UK and CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK †Corresponding author. Email: [email protected] Edited by: Ernesto Estrada [Received on 16 October 2013; accepted on 23 April 2014] In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such ‘multilayer’ features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize ‘traditional’ network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion.
    [Show full text]
  • A Survey of Adversarial Learning on Graph
    A Survey of Adversarial Learning on Graph LIANG CHEN, Sun Yat-sen University of China JINTANG LI, Sun Yat-sen University of China JIAYING PENG, Sun Yat-sen University of China TAO XIE, Sun Yat-sen University of China ZENGXU CAO, Hangzhou Dianzi University of China KUN XU, Sun Yat-sen University of China XIANGNAN HE, University of Science and Technology of China ZIBIN ZHENG∗, Sun Yat-sen University of China Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classication, link prediction and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, a line of studies have emerged for both aack and defense addressed in dierent graph analysis tasks, leading to the arms race in graph adversarial learning. Despite the booming works, there still lacks a unied problem denition and a comprehensive review. To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically. Specically, we survey and unify the existing works w.r.t. aack and defense in graph analysis tasks, and give appropriate denitions and taxonomies at the same time. Besides, we emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively. Hopefully, our works can provide a comprehensive overview and oer insights for the relevant researchers. More details of our works are available at hps://github.com/gitgiter/Graph-Adversarial-Learning. CCS Concepts: •Computing methodologies ! Semi-supervised learning settings; Neural networks; •Security and privacy ! So ware and application security; •Networks ! Network reliability; Additional Key Words and Phrases: adversarial learning, graph neural networks, adversarial aack and defense, adversarial examples, network robustness ACM Reference format: Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, and Zibin Zheng.
    [Show full text]
  • Evolving Networks and Social Network Analysis Methods And
    DOI: 10.5772/intechopen.79041 ProvisionalChapter chapter 7 Evolving Networks andand SocialSocial NetworkNetwork AnalysisAnalysis Methods and Techniques Mário Cordeiro, Rui P. Sarmento,Sarmento, PavelPavel BrazdilBrazdil andand João Gama Additional information isis available atat thethe endend ofof thethe chapterchapter http://dx.doi.org/10.5772/intechopen.79041 Abstract Evolving networks by definition are networks that change as a function of time. They are a natural extension of network science since almost all real-world networks evolve over time, either by adding or by removing nodes or links over time: elementary actor-level network measures like network centrality change as a function of time, popularity and influence of individuals grow or fade depending on processes, and events occur in net- works during time intervals. Other problems such as network-level statistics computation, link prediction, community detection, and visualization gain additional research impor- tance when applied to dynamic online social networks (OSNs). Due to their temporal dimension, rapid growth of users, velocity of changes in networks, and amount of data that these OSNs generate, effective and efficient methods and techniques for small static networks are now required to scale and deal with the temporal dimension in case of streaming settings. This chapter reviews the state of the art in selected aspects of evolving social networks presenting open research challenges related to OSNs. The challenges suggest that significant further research is required in evolving social networks, i.e., existent methods, techniques, and algorithms must be rethought and designed toward incremental and dynamic versions that allow the efficient analysis of evolving networks. Keywords: evolving networks, social network analysis 1.
    [Show full text]
  • Dynamic Network Analysis (Spring 2020) Course 17-801, 17‐685, 19‐640 Instructor Prof
    Dynamic Network Analysis (Spring 2020) Course 17-801, 17-685, 19-640 Instructor Prof. Kathleen M Carley Phone: x86016 Office: Wean 5130, ISR E‐Mail: [email protected] Office Hours: by appointment; contact Sienna Watkins for scheduling [email protected] Teaching Assistant Mihovil Bartulovic, [email protected], Wean 4211 Introduction Who knows who? Who knows what? Who communicates with whom? Who is influential? How do ideas, diseases, and technologies propagate through groups? How do social media, social, knowledge, and technology networks differ? How do these networks evolve? How do network constrain and enable behavior? How can a network be compromised or made resilient? What are network cascades? Such questions can be addressed using Network Science. Network Science, a.k.a. social network analysis, link analysis, geo‐network analysis, and dynamic network analysis is a fast‐growing interdisciplinary field aimed at understanding simple & high dimensional networks, from both a static and a dynamic perspective. Across an unlimited application space, graph theoretic, statistical, & simulation methodologies are used to reason about complex systems as networks. An interdisciplinary perspective on network science is provided, with an emphasis on high‐dimensional dynamic data. The fundamentals of network science, methods, theories, metrics & confidence estimation, constraints on data collection & bias, and key research findings & challenges are examined. Illustrative networks discussed include social media based (e.g., twitter), disaster response, organizational, semantic, political elite, crises, terror, & P2P networks. Critical procedures covered include: basic centralities and metrics, group and community detection, link inference, network change detection, comparative analytics, and big data techniques. Applications from business, science, art, medicine, forensics, social media & numerous other areas are explored.
    [Show full text]
  • Advanced Topics in Social Dynamics Family Demography Session 1(9.03
    Advanced topics in social dynamics Family Demography Gøsta Esping-Andersen Bocconi 2020-21 Session 1(9.03.2021): Theoretical perspectives in the study of the family In this session we will focus on key theories within family research, from both economics and sociology. Required readings: Becker, G (1973). ‘A theory of marriage. Part 1’. Journal of Political Economy, 81: 4: 813-46 Lesthaeghe, R. (2010).‘The unfolding story of the second demographic transition’. Population and Development Review, 36(2), 211-251. Recommended readings: Esping-Andersen, G and Billari, F. 2015 ‘Retheorizing family demographic change’. Population and Development Review, 41: 1-31 McDonald, P 2013 ‘Societal foundations for explaining fertility’. Demographic Research, 28: 981-994 Lundberg, Shelly and Pollack, Robert. 2007. "The American Family and Family Economics." Journal of Economic Perspectives 21(2): 3-26. Oppenheimer, Valerie Kincade. 1988. "A Theory of Marriage Timing." American Journal of Sociology 94(3): 563-91. Session 2 (10.03). Trends in gender roles, family behavior and structure Required readings: Esping-Andersen, G 2016 Families in the 21st Century. Chapter 1: ‘The return of the family’ (this book can be downloaded gratis from SNS’ website ([email protected]) Goldin, M 2006. "The Quiet Revolution That Transformed Women’s Employment, Education, and Family." American Economic Review 96(2): 1-21. Recommended readings: (for student presentation: minimally the Sevilla-Sanz article and the van Bavel et.al. paper) Van Bavel, J 2018 ‘The reversal of the gender gap in education and its consequences for family life’. Annual Review of Sociology, 44: 341-60 England, P. Et.al., 2020 ‘Progress toward gender equality in the United States has slowed or stalled’.
    [Show full text]
  • Manifesto of Computational Social Science R
    Manifesto of computational social science R. Conte, N. Gilbert, G. Bonelli, C. Cioffi-Revilla, G. Deffuant, J. Kertesz, V. Loreto, S. Moat, J.P. Nadal, A. Sanchez, et al. To cite this version: R. Conte, N. Gilbert, G. Bonelli, C. Cioffi-Revilla, G. Deffuant, et al.. Manifesto of computational social science. The European Physical Journal. Special Topics, EDP Sciences, 2012, 214, p. 325 - p. 346. 10.1140/epjst/e2012-01697-8. hal-01072485 HAL Id: hal-01072485 https://hal.archives-ouvertes.fr/hal-01072485 Submitted on 8 Oct 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Eur. Phys. J. Special Topics 214, 325–346 (2012) © The Author(s) 2012. This article is published THE EUROPEAN with open access at Springerlink.com PHYSICAL JOURNAL DOI: 10.1140/epjst/e2012-01697-8 SPECIAL TOPICS Regular Article Manifesto of computational social science R. Conte1,a, N. Gilbert2, G. Bonelli1, C. Cioffi-Revilla3,G.Deffuant4,J.Kertesz5, V. Loreto6,S.Moat7, J.-P. Nadal8, A. Sanchez9,A.Nowak10, A. Flache11, M. San Miguel12, and D. Helbing13 1 ISTC-CNR, Italy 2 CRESS, University
    [Show full text]
  • Citron Research Backing up the Sleigh on Facebook – 2019 S&P
    December 26, 2018 Citron Research Backing Up the Sleigh on Facebook – 2019 S&P Stock of the Year Separating the Noise from Reality will take Facebook back to $160 Two and a half years ago, Citron Research said that FB was a long-term short and that engagement levels would eventually top out. At the time, the stock was trading in the $120 range. In the past 30 months FB has more than doubled its quarterly revenue and concerns of engagement have shifted to concerns of addiction, yet the stock is back down in $120 range. Time to back up the sleigh! Yet, 2018 is a year that Facebook shareholders would like to forget, as the stock is down 30% YTD and over 40% from its recent high. Yet, while the media has reported on one scandal after the next and the NYT even tried to promote a #deletefacebook movement. The truth is revenues and more important user base has seen little impact. As you read this article Facebook has 2.2 billion active users and has grown revenues 33% q over q during this controversial 12 months. Considering the user base excludes China, this is a lead that has no second place. Going Forward There are only three talking points on Facebook that matter: 1. Putting Facebook Valuation in Perspective 2. Is Facebook Evil or Investible? 3. Instagram – the Anti CRaP experience © Copyright 2018 | Citron Research | www.citronresearch.com | All Inquiries – [email protected] December 26, 2018 Valuation in Perspective Facebook is growing faster than 95% of the S&P with margins higher than about 90% of the S&P.
    [Show full text]