DOCSLIB.ORG

Presented to the Faculty of the School of Engineering and Applied Science University of Virginia

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Presented to the Faculty of the School of Engineering and Applied Science University of Virginia Stepping Back from the Trees to see the Forest: Network Approaches to Valuing Intelligence A Dissertation Presented to the faculty of the School of Engineering and Applied Science University of Virginia in partial fulfillment of the requirements for the degree Doctor of Philosophy by Christopher M. Smith May 2013 APPROVAL SHEET The dissertation is submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy AUTHOR The dissertation has been read and approved by the examining committee: Prof. William T. Scherer Advisor Prof. Peter A. Beling Prof. Michael Smith Prof. Jeremy Marcel Prof. Duncan E. McGill Accepted for the School of Engineering and Applied Science: Dean, School of Engineering and Applied Science May 2013 Abstract Determining value of intelligence can be a difficult problem. One way to value intelligence is to judge a document’s worth by its location within a structure of a given corpus of documents. Network applications are a natural extension of this logic. I introduce a methodology for value of information (VOI) for networks, comparable to VOI for influence diagrams. Additionally, citation networks and Google’s PageRank algorithm are examples of valuing information based on its location within a structure. Dynamic network analysis (DNA) has been used to allow social network analysis (SNA) involving multi-nodal networks by creating inferences across networks with common nodes. I introduce the application of the DNA layered approach to information networks in an attempt to determine value of intelligence. These applications demonstrate supplemental, and objective ways of measuring intelligence. iii For my wife, daughters and son. With hard work, all things are possible. iv Notation and Acronyms ACM Association for Computing Machinery CASOS Center for Computational Analysis of Social and Organizational Systems CIA Central Intelligence Agency CRS Congressional Research Service DARPA Defense Advanced Research Projects Agency DIA Defense Information Agency DNA Dynamic Network Analysis DNI Director of National Intelligence EVII Expected value of imperfect information EVPI Expected value of perfect information G2 Intelligence staff section IC Intelligence Community IED Improvised Explosive Device IEW/TA Intelligence, Electronic Warfare, and Target Acquisition INSCOM Intelligence and Security Command IR Intelligence Report ISR Intelligence, Surveillance, and Reconnaissance JHU/APL Johns Hopkins University/Applied Physics Laboratory JT-COIC Joint Training Counter IED Operations Integration Center JWICS Joint Worldwide Intelligence Communications System MC Main Component MORS Military Operations Research Society NDIC National Defense Intelligence College NGIC National Ground Intelligence Center NPV Net Present Value ODNI Office of the Director of National Intelligence OPTVIEW Operational Value of Intelligence, Electronic Warfare, and Target Acquisition OR Operations Research PPBES Planning, Programming, Budgeting, and Execution System SNA Social Network Analysis VOI Value of Information v Table of Contents Approval ....................................................................................................................................................... ii Abstract ........................................................................................................................................................ iii Notation and Acronyms ................................................................................................................................ v List of Tables ............................................................................................................................................... ix List of Figures ............................................................................................................................................... x 1 Introduction ........................................................................................................................................... 1 1.1 Motivation ..................................................................................................................................... 1 1.2 Purpose and Scope ........................................................................................................................ 2 1.3 Organization of Document ............................................................................................................ 2 2 Background ........................................................................................................................................... 3 2.1 Introduction ................................................................................................................................... 3 2.2 Definition of Intelligence .............................................................................................................. 5 2.3 Approaches to Valuing Information ............................................................................................. 7 2.4 Decision Focused Methods ........................................................................................................... 9 2.5 Review of current valuation of intelligence ................................................................................ 19 2.6 Problems applying existing methodologies to Intelligence......................................................... 25 2.7 Value within the Intelligence Cycle ............................................................................................ 26 2.8 Future work and Conclusion ....................................................................................................... 29 3 Value of Information Applied to Networks ........................................................................................ 30 3.1 Introduction ................................................................................................................................. 30 3.2 Background ................................................................................................................................. 31 3.3 Value of Information (VOI) Applied to Networks ..................................................................... 34 3.3.1 Deterministic Sensitivity Analysis Approach ..................................................................... 34 3.4 Notional Example ....................................................................................................................... 36 3.4.1 Data set ................................................................................................................................ 37 3.4.2 Problem ............................................................................................................................... 38 3.4.3 Notional Solution ................................................................................................................ 38 3.5 Application of methodology ....................................................................................................... 38 3.6 Results ......................................................................................................................................... 40 3.7 Conclusion / Future Work ........................................................................................................... 41 vi 3.8 Introducing intelligence Potential ............................................................................................... 42 3.8.1 A notional application of finding Potential ......................................................................... 43 3.9 Future Research .......................................................................................................................... 44 4 Layered Network Approach ................................................................................................................ 45 4.1 Introduction ................................................................................................................................. 45 4.2 Background ................................................................................................................................. 46 4.3 DNA concepts applied to information networks in the intelligence field ................................... 48 4.4 Intelligence questions applied to information networks ............................................................. 51 4.5 Applied to data set ...................................................................................................................... 54 5 Analysis/Results .................................................................................................................................. 55 5.1 Turing award winners vs. non-winners ....................................................................................... 56 5.2 Top recurring ranked authors ...................................................................................................... 58 5.3 The visible effect of using different matrices ............................................................................. 59 5.4 Analysis of key word popularity ................................................................................................. 62 5.5 Papers to translate ....................................................................................................................... 64 5.6 Discussion and Analysis of Results ...........................................................................................
Load more

Recommended publications
  • 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]
  • 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]
  • 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]
  • Self-Organized Terrorist- Counterterrorist Adaptive Coevolutions, Part I
    CRM D0010776.A3/1Rev February 2005 Self-Organized Terrorist- Counterterrorist Adaptive Coevolutions, Part I: A Conceptual Design Andrew Ilachinski 4825 Mark Center Drive • Alexandria, Virginia 22311-1850 Approved for distribution: February 2005 Dr. Gregory M. Swider Director, Tactical Analysis Team Operations Evaluation Group This document represents the best opinion of CNA at the time of issue. It does not necessarily represent the opinion of the Department of the Navy. Approved for Public Release; Distribution Unlimited. Specific authority: N00014-00-D-0700. For copies of this document call: CNA Document Control and Distribution Section at 703-824-2123. Copyright 2005 The CNA Corporation [Osama bin Laden is a] “...product of a new social structure; a new social feeling in the Muslim world. Where you have strong hostility not only against America, but also against many Arab and Muslim regimes who are allying to America... and that's why, if bin Laden was not there, you would have another bin Laden. You would have another name, with the same character, with the same role, of bin Laden... ...That's why we call it a phenomena, not a person.”1 “Osama bin Laden’s most brilliant stroke may well have been to allow the global Salafi Jihad network to evolve spontaneously and naturally, and not interfere too much with its evolution, except to guide it through incentives because of his control of resources. The system developed into a small-world network with robustness and flexibility and became more militant and global for both internal and external reasons.”2 1.Extract from an interview with Saad al Fagih (a London-based Saudi Arabian exile who heads the Movement for Islamic Reform in Arabia), on Public Broadcasting Service's Frontline, 1999.
    [Show full text]
  • A Statistical Network Analysis of Underground Punk Worlds in Manchester and Liverpool 2013-2015
    The Persistence of Punk Rock: A Statistical Network Analysis of Underground Punk Worlds in Manchester and Liverpool 2013-2015 A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Humanities 2019 Joseph B. Watson School of Social Sciences Contents Abstract………………………………………………………………………..6 Declaration…………………………………………………………………….7 Copyright Statement…………………………………………………………....7 The Author…………………………………………………………………….8 Acknowledgements…………………………………………………………….8 Chapter One: Punk, Networks and the North West……………………………9 1.1 Introduction……………………………………………………...9 1.2 What is a Music World?…………………………………………11 1.3 What is Punk Music?……………………………………………12 1.3.1 Defining Punk Music…………………………………….13 1.3.2 My Experience of Punk Music as a Participant…………...14 1.3.3 Underground Punk in a Modern UK Context……………15 1.4 Manchester and Liverpool as Musical Cities…………………….17 1.5 Social Network Analysis………………………………………...18 1.5.1 The Network Lens……………………………………….18 1.5.2 The Analytical Toolbox…………………………………..19 1.6 Thesis Structure…………………………………………………19 Chapter Two: Music Worlds………………………………………………….22 2.1 Introduction…………………………………………………….22 2.2 Subculture………………………………………………………22 2.3 Towards Art Worlds…………………………………………….25 2.4 Collective Activity………………………………………………26 2.5 Conventions…………………………………………………….26 2.6 Resources……………………………………………………….28 2.7 Venues………………………………………………………….30 2.8 Music Making…………………………………………………...32 2.9 Underground Punk in Manchester and Liverpool……………….33 2.10 Social Networks and Music Worlds……………………………..34
    [Show full text]
  • Introduction to Social Network Analysis (SNA) As an Investigative Tool
    Trends Organ Crim (2009) 12:101–121 DOI 10.1007/s12117-008-9057-6 ORIGINAL ARTICLE Introduction to Social Network Analysis (SNA) as an investigative tool Renée C. van der Hulst Published online: 13 December 2008 # Springer Science + Business Media, LLC 2008 Abstract Social behavior is brought about mainly through social ties and connections. Our contacts with other people shape our view of the world, reinforce our identity, and the interactions provide us with all kinds of opportunities and resources to get things done. The social capital associated with networks is also one of the primary ways facilitating crime. Therefore, the systematic analysis of criminal networks is considered a viable means to gain a more thorough understanding of criminal behavior. This paper is a general introduction to social network analysis (SNA) as an analytical tool for the study of adversary networks. The paper reviews some theoretical and key concepts, highlights functional applications, and presents a tentative protocol for data handling and coding. The discussion deals with some methodological issues, challenges and future developments in the field. Keywords Social Network Analysis (SNA) . Organized crime . Terrorism . Social capital . Methods . Criminal investigation Organized crime is a relational phenomenon that involves multiple actors. For quite some time, organized crime groups were considered to be durable groups of at least three offenders, involved in several types of offences with the primary motive to gain financial profits. Contrary to earlier assumptions, organized crime in the Netherlands turns out to be less hierarchically and much more loosely structured and it is characterized by a logistic web in which many smaller networks are active (Kleemans et al.
    [Show full text]
  • Sustainable Enrollment Management: a Dynamic Network Analysis Forrest Murray Stuart Clemson University
    Clemson University TigerPrints All Dissertations Dissertations 8-2016 Sustainable Enrollment Management: A Dynamic Network Analysis Forrest Murray Stuart Clemson University Follow this and additional works at: https://tigerprints.clemson.edu/all_dissertations Recommended Citation Stuart, Forrest Murray, "Sustainable Enrollment Management: A Dynamic Network Analysis" (2016). All Dissertations. 1697. https://tigerprints.clemson.edu/all_dissertations/1697 This Dissertation is brought to you for free and open access by the Dissertations at TigerPrints. It has been accepted for inclusion in All Dissertations by an authorized administrator of TigerPrints. For more information, please contact [email protected]. SUSTAINABLE ENROLLMENT MANAGEMENT: A DYNAMIC NETWORK ANALYSIS A Dissertation Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Educational Leadership by Forrest Murray Stuart August 2016 Accepted by: Dr. Russ Marion, Committee Chair Dr. Jon Christiansen Dr. Robert Knoeppel Dr. James Satterfield ABSTRACT The primary purpose of this study was to investigate higher education enrollment management (EM) as a complex adaptive system (CAS) and to provide colleges and universities a foundational understanding of what a sustainable EM system looks like from a Dynamic Network Analysis (DNA) perspective. Additionally, I aimed to describe how formal network structures either promote or inhibit sustainable EM. To this end, the following research questions guided this study: 1. To what extent is the research site organized to enable effective and efficient information flow? 2. Which combinations of independent network measures (adaptive leadership, social capital, and clique structure) produce optimal outcome measures for a sustainable EM system? The research design was an exploratory, sequential mixed methods design, and the research methodologies used for data analysis were DNA and Response Surface Methods (RSM).
    [Show full text]
  • A Network Analysis of Information Use in a Public Health Organization
    A Network Analysis of Information Use in a Public Health Organization by Jacqueline Carty Merrill~Matzner Submitted in partial fulfillment of the requirement for the degree of Doctor of Nursing Science in the School of Nursing COLUMBIA UNIVERSITY 2006 © 2006 Jacqueline Carty Merrill~Matzner All Rights Reserved ABSTRACT A Network Analysis of Information Use in a Public Health Organization Jacqueline Carty Merrill~Matzner Organizational network analysis was used to study information use in a health department. Public health performance depends on specialized information that travels throughout an organization via communication networks among employees. In most cases, the interactions that occur within these networks are poorly understood and unmanaged. The goals of this study were to determine what links existed between information use and performance, and to assess organizational network analysis as a tool for public health management. Data on communication links among the health department’s staff was obtained via survey, with a 93% response rate. Data on resources, tasks and knowledge was obtained from a concurrent research study for secondary analysis. These data were configured in matrices: agent x agent, agent x resources, agent x task, agent x knowledge, and agent x external organizations. These were analyzed as meta-matrix using Organizational Risk Analyzer (ORA) software. This produced reports at the individual, program, and organization level. The results yielded graphical representations of network structure and statistical reports on: quality of the information network; employees in key positions in the network; the status of experienced staff; and an analysis of a planned merger of two divisions. Findings revealed problems in information flow, including the likelihood that sub groups are controlling knowledge and resources; overspecialization of knowledge; potential for significant knowledge loss through retirement; little back up for personnel turnover; and informational silos.
    [Show full text]
  • New Approaches for Performance Optimization and Analysis of Large-Scale Dynamic Social Network Analysis Using Anytime Anywhere Algorithms
    New Approaches for Performance Optimization and Analysis of Large-Scale Dynamic Social Network Analysis using Anytime Anywhere Algorithms Eunice E. Santosa, Vairavan Murugappana and John Korahb a School of Information Sciences, University of Illinois at Urbana-Champaign b Department of Computer Science, California State Polytechnic University Pomona Dynamic Large Scale Social Network Analysis Social Network Analysis ❖Dramatic increase in the availability of dynamic data from various information sources ❖ Example: Social media networks, smart sensors, stock market, etc. ❖Facebook (over 2.3 billion active usersa), Twitter (300 million active usersb), LinkedIn (610 million usersc) ❖ Network dynamism - relationships, followers and connections ❖ Continuously evolving networks ❖Opportunities and Applications: ❖ Large datasets significantly extends our understanding of underlying social phenomenon ❖ Disaster management, Health care, Business analytics 푎 http://newsroom.fb.com/company-info/ b https://about.twitter.com/company c https://press.linkedin.com/about- linkedin Dynamic Large Scale Social Network Analysis Challenges Network size: – Computation time and resources increases dramatically with network size – Restricts the utility of social network analysis (SNA) in time critical applications Network Dynamism: – On average about 500 million new tweets every daya – In many real-time social media analytics and disaster management, the underlying network is evolving – Restarting or analyzing static snapshot of the network will often yield poor
    [Show full text]
  • ORA: a Toolkit for Dynamic Network Analysis and Visualization
    ORA: A Toolkit for Dynamic Network Analysis and Visualization Kathleen M. Carley Professor Carnegie Mellon University Pittsburgh PA 15213 USA [email protected] CEO Netanomics Sewickley PA 15143 USA [email protected] Synonyms ORA, ORA Toolkit, ORA-Lite, ORA-PRO, *ORA, ORA-NetScenes – note ORA is not an acronym Glossary Social Network Analysis: graphical, statistical and visualization metrics, algorithms and techniques for analysing structural data that can be represented as nodes and relations. Social network analysis is also referred to as network analysis, dynamic network analysis, network science, SNA, and DNA. Social Media: data generated by an on-line social networking tools such as Twitter, Facebook or Foursquare. Social media networks are networks derived from social media data such as the Twitter retweet network. Social media are also referred to as on-line sources, open-source, e-media. Dynamic Networks: networks that vary through time. An example is the network of who talks to whom within a company by day. Dynamic networks are also referred to as temporal networks, time variant networks, and dynamical networks. Spatial Networks: networks embedded in space such that each node has one or more locations at which it occurs. An example is the network of who interacts with whom among political activists such that each activist is also linked to the locations at which they have taken part in *Length: Long Essays: 3600-6000 words or 20,000 -40,000 characters with space (approx. 5-10 printed pages) Illustrations are welcome, this will be mentioned in the guidelines demonstrations. Spatial networks are also referred to as geo-spatial networks, geo-intelligence networks, geographical networks, networks through space, and spatially embedded networks.
    [Show full text]
  • Computational Models for Social Network Analysis: a Brief Survey
    Computational Models for Social Network Analysis: A Brief Survey Jie Tang Department of Computer Science and Technology, Tsinghua University Tsinghua National Laboratory for Information Science and Technology (TNList) [email protected] ABSTRACT Computational Social Science [Giles] 2012 With the exponential growth of online social network services such Computational Social Science [Lazer et al.] 2009 as Facebook and Twitter, social networks and social medias become 2009- Spread of Obesity, Smoking, Happiness 2007 [Christakis, Fowler] more and more important, directly influencing politics, economics, 2005 Densification [Leskovec, Kleinberg, Faloutsos] and our daily life. Mining big social networks aims to collect and 2003 Link Prediction [Liben-Nowell, Kleinberg] Influence Maximization [Kempe,Kleinberg,Tardos] analyze web-scale social data to reveal patterns of individual and 2002 Community Detection [Girvan, Newman] group behaviors. It is an inherently interdisciplinary academic field which emerged from sociology, psychology, statistics, and graph Scale Free [Barabási,Albert & Faloutsos et al.] 1999 Small World [Watts, Strogatz] 1998 theory. In this article, I briefly survey recent progress on social Hubs & Authorities [Kleinberg] 1997 network mining with an emphasis on understanding the interac- 1995 Structural Hole [Burt] tions among users in the large dynamic social networks. I will start 1992 Dunbar’s Number [Dunbar] with some basic knowledge for social network analysis, including 1973 Weak Tie [Granovetter] methodologies and tools for macro-level, meso-level and micro- 1967 Six Degrees of Separation [Milgram] level social network analysis. Then I will give an overall roadmap 1 of social network mining. After that, I will describe methodologies Figure 1: History of social network research. for modeling user behavior including state-of-the-art methods for learning user profiles, and introduce recent progress on modeling dynamics of user behaviors using deep learning.
    [Show full text]