1,721,017 research outputs found
The Web Science Observatory
No full textTo understand and enable the evolution of the Web and to help address grand societal challenges, the Web must be observable at scale across space and time. That requires a globally distributed and collaborative Web Observatory
Exploring Peer Prestige in Academic Hiring
Get PDFWhy do we care about prestige rankings? What does this preoccupation say about our implicit understanding of prestige as a function of image and identity? For an academic community in which identity matters, prestige rankings reveal an important dimension of identity in community context. In the case of existing rankings for the emergent iSchools, interdisciplinary growth has rendered the community context incomplete.
Exploring indicators of prestige in hiring networks as related to the measures of prestige presented in peer rankings such as US News & World Report (USNWR) rankings provides a new perspective on hiring and identity in the iSchools. This research collected data on the educational pedigrees of 693 full-time faculty at iSchools and constructed a hiring network of institutional affiliations, with connections between the schools based on the institutions from which current iSchool faculty received their PhD degrees. The study quantitatively and qualitatively compares the iSchool hiring network structure to a similar hiring network in the more established academic discipline of Computer Science (CS), and uses regression on network prestige and centrality measures to explain the variance in USNWR ratings. The study projects inclusive prestige ratings for the full CS and iSchool communities, which reveal underlying similarities in the structure of the two networks. Analysis of additional hiring network features, such as faculty areas of study and self-hiring in the iSchools, demonstrates the interdisciplinary diversity of the emergent field of information and its constituent institutions.Master of Science (MS)School of InformationUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/62470/1/AWiggins-MTOPthesis-DeepBlue(2).pd
SI 508 - Networks: Theory and Application, Fall 2008
No full textSI 508 has been taught in various forms from 2006 to 2008 to master's students at the University of Michigan School of Information. The course covers topics in network analysis, from social networks to applications in information networks such as the Internet. I will introduce basic concepts in network theory, discuss metrics and models, use software analysis tools to experiment with a wide variety of real-world network data, and study applications to areas such as information retrieval. -- As a network scientist I think networks are fun to talk about, but they are even more fun to play with. Therefore, labs are an integral part of this course. In addition to providing background material, the labs and the demos offer ample opportunity for learners to get hands-on with interactive demonstrations, real-world data sets, and a dizzying array of tools (Pajek, Guess, NetLogo, and others). Experimenting in the labs will enable learners to get much more out of this course than simply reading the lectures and other materials. The labs are also designed to bring you up to speed with the skills you need to do the assignments. -- Another important part of the course is the final group project, in which students take the concepts they learned and apply them to networks that they select. Although I can offer little guidance on anyone's individual project through this open format, I hope that the assignments will expose people to different techniques one can apply and various questions to explore. This showcase of student projects from past years should provide some inspiration. Enjoy, and tell Open.Michigan what you think about the course by going to the course feedback
link on the left.http://deepblue.lib.umich.edu/bitstream/2027.42/64962/5/ERdiffuonThumb6.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/6/reliencethumb12.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/7/WSSISThumb8.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/8/GraphColoringThumb1.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/9/OpinionFormationThumb5.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/10/pagerankthumb10.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/11/PrefGrowthThumb3.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/12/BAdiffuonThumb7.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/13/GiantComponentThumb2.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/14/LatticePercolationThumb13.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/15/SmallWorldSearch16.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/16/SimpleComplexSpreadThumb9.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/17/BetClustThumb4.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/18/LexRankTumb11.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/19/GamesEBThumb15.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/20/GraphColoringThumb14.jpghttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/21/SI508-F08-Week4.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/22/SI508-F08-Week10.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/23/SI508-F08-Week8.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/24/SI508-F08-Week9.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/25/SI508-F08-Week5.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/26/SI508-F08-Week9.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/27/SI508-F08-Week8.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/28/SI508-F08-Week3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/29/SI508-F08-Week11.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/30/SI508-F08-Week2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/31/SI508-F08-Week1.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/32/SI508-F08-Week12.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/33/SI508-F08-Week3.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/34/SI508-F08-Week5.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/35/SI508-F08-Week11.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/36/SI508-F08-Week1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/37/SI508-F08-Week4.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/38/SI508-F08-Week12.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/39/SI508-F08-Week2.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/40/SI508-F08-Week10.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/41/SI508-F08-syllabus.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/42/SI508-F08-syllabus.dochttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/43/SI508-F08-midterm.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/44/SI508-F07-midterm-solution.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/45/SI508-F08-midterm-solution.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/46/SI508-F07-midterm.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/47/week10.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/48/week12.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/49/week01.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/50/week06.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/51/week07.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/52/week02.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/53/week08.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/54/midterm-exams.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/55/week09.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/56/week05.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/57/week11.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/58/week03.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/59/week04.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/60/ExerciseII.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/61/zachary.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/62/zacharykarate.gdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/63/SI508-F08-Week7-Lab6.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/64/week7labREADME.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/65/poliblogmfinder.txthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/66/SI508-F08-Week7-Lab6.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/67/createfacebookgdf.plhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/68/triad_undir.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/69/betweennessclustering2.pyhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/70/userschool.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/71/schooltag.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/72/SI508-F08-Week11-Lab10.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/73/usertag.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/74/lab10instructions.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/75/ischool.gdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/76/SI508-F08-Week11-Lab10.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/77/SI508-F08-Week6-Lab5.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/78/SI508-F08-Week6-Lab5.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/79/week10-diffuon.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/80/pageranktoolW.pyhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/81/week9-IR.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/82/words.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/83/test.gdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/84/logbinfromlinbin.mhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/85/fitlineonloglog.mhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/86/PLmaxlikelihood.mhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/87/hdrload.mhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/88/randompowerlawints.mhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/89/binvector.mhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/90/distributions-in-matlabhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/91/SI508-F08-Week5-Lab4.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/92/cumulativecounts.mhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/93/SI508-F08-Week5-Lab4.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/94/week5-growing.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/95/SI508-F08-Week3-Lab2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/96/SI508-F08-Week3-Lab2.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/97/SI508-F08-Week8-Lab7.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/98/SI508-F08-Week8-Lab7.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/99/readnexusPC.plhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/100/instructionsForFacebook.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/101/SI508-F08-Week2-Lab1.ppthttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/102/SI508-F08-Week2-Lab1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/103/readnexusMAC.plhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/104/actorsandmovies.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/105/actorsandmoviesWithGere.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/106/nexusanon.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/107/week4-smallworldnetworks.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/108/gnutella.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/109/SI508-F08-Week7-Asgnment5.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/110/.DS_Storehttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/111/readnexusnomultiple.plhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/112/SI508-F08-Week7-Asgnment5.dochttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/113/poliblogrecip.gdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/114/.DS_Storehttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/115/SI508-F08-Week11-Asgnment8.dochttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/116/SI508-F08-Week11-Asgnment8.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/117/SI508-F08-Week6-Asgnment4.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/118/.DS_Storehttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/119/SI508-F08-Week6-Asgnment4.dochttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/120/poliblog.gdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/121/SI508-F08-Week12-Asgnment9.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/122/reliencebetweenness.pyhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/123/gnutella2.gdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/124/SI508-F08-Week12-Asgnment9.dochttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/125/reliencedegree.pyhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/126/SI508-F08-Week10-Asgnment7.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/127/.DS_Storehttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/128/SI508-F08-Week10-Asgnment7.dochttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/129/SI508-F08-Week1-Asgnment1.dochttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/130/SI508-F08-Week1-Asgnment1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/131/SI508-F08-Week3-Asgnment3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/132/SI508-F08-Week3-Asgnment3.dochttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/133/.DS_Storehttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/134/CitationNetWoCoauthors.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/135/SI508-F08-Week8-Asgnment6.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/136/AuthorCitationNetwork.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/137/.DS_Storehttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/138/CoAuthorshipNetwork.nethttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/139/SI508-F08-Week8-Asgnment6.dochttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/140/SI508-F08-Week2-Asgnment2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/141/.DS_Storehttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/142/SI508-F08-Week2-Asgnment2.dochttp://deepblue.lib.umich.edu/bitstream/2027.42/64962/143/si508-fall2008.zi
Longevity in Second Life
No full textUser retention is important to the success of online social media, particularly in virtual world settings where users shape one another’s online experience. We study a rich set of variables, including social network and group membership, chatting, and transactions, in order to predict which users will stay and which ones will leave. We find that simple variables directly measuring the intensity and diversity of a user’s interaction with others are most predictive
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Network Migration Strategies: Evaluating Performance with Extensions of Data Envelopment Analysis.
Get PDFIn the current economic climate, many companies are facing pressures to operate more efficiently resulting in frequent mergers, acquisitions, and reorganizations. This often necessitates a change in various network structures within these organizations, which leads to the guiding question of this study. How does an organization efficiently and effectively transition its network structures while making use of multiple performance measures?
This dissertation seeks to develop models and algorithms, based on Data Envelopment Analysis (DEA), to analyze perturbations in real-world network topologies. The first part of this dissertation shows the historical development of DEA with an introduction of notation and extensions. The next chapter is a detailed study of US airport inefficiency, which answers the question: Is there a difference in the efficiency of hub and non-hub airports in the United States? After decomposing the efficiency into scale efficiency, mixed efficiency and pure technical efficiency, the Wilcoxon Rank Sum test is able to show that there are differences between hub and non-hub airports.
The fourth section of the dissertation develops a theoretical model, the Inverse Range-based Directional Distance model (INVRDD-DEA), to address the presence of reverse quantities in DEA and yield shortest path projections. This model leads to the Fully Comprehensive RDD-DEA model that takes into account all sources of inefficiency. This model is then used to illustrate all sources of inefficiency in a greenhouse gas example.
The final contribution of the dissertation is an exploration into the evolution of the operations research approach to the field of network science. The concept of re-engineering of networks, defined as the ability to optimize perturbations to existing networks based on several performance metrics, is used as a methodological model for typical types of changes that exist in cooperate networks. The critical factors for building an algorithm for modifying network topologies are identified and used to design a procedure for making changes in networks. Finally, an example of an ERP implementation is given to show the benefits of using DEA to make changes to existing network topologies.PhDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75909/1/wsutton_1.pd
Statistical Techniques for Exploratory Analysis of Structured Three-Way and Dynamic Network Data.
Get PDFIn this thesis, I develop different techniques for the pattern
extraction and visual exploration of a collection of data matrices.
Specifically, I present methods to help home in on and visualize an
underlying structure and its evolution over ordered (e.g., time) or
unordered (e.g., experimental conditions) index sets. The first part
of the thesis introduces a biclustering technique for such three
dimensional data arrays. This technique is capable of discovering
potentially overlapping groups of samples and variables that evolve
similarly with respect to a subset of conditions. To facilitate and
enhance visual exploration, I introduce a framework that utilizes
kernel smoothing to guide the estimation of bicluster responses over
the array. In the second part of the thesis, I introduce two matrix
factorization models. The first is a data integration model that
decomposes the data into two factors: a basis common to all data
matrices, and a coefficient matrix that varies for each data matrix.
The second model is meant for visual clustering of nodes in dynamic
network data, which often contains complex evolving structure. Hence,
this approach is more flexible and additionally lets the basis evolve
for each matrix in the array. Both models utilize a regularization
within the framework of non-negative matrix factorization to encourage
local smoothness of the basis and coefficient matrices, which improves
interpretability and highlights the structural patterns underlying the
data, while mitigating noise effects. I also address computational
aspects of applying regularized non-negative matrix factorization
models to large data arrays by presenting multiple algorithms,
including an approximation algorithm based on alternating least
squares.PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99838/1/smankad_1.pd
Understanding and Augmenting Expertise Networks.
Get PDFThis thesis investigates large scale knowledge searching and sharing processes in online communities and organizations. It focuses on understanding the relationship between social networks and expertise sharing activities. The work explores design opportunities of these social networks to bootstrap knowledge sharing, by using the specific social characteristics of social networks which can lead to sizeable differences in the way expertise is searched and shared. The potential impact of this approach was examined in three related studies using data from Java Forum, Yahoo Answers, and Enron.
The Java Forum study investigated how people asked and answered questions in this online community using advanced social network analysis metrics. Furthermore, it explored algorithms that made use of the network structure to evaluate expertise levels. It also used simulations to explore possible social structures and dynamics that would affect the interaction patterns and network structure in online communities. The Yahoo Answers study extended the Java Forum study into a more general community setting and covered much more diverse knowledge sharing dynamics. It analyzed both content properties and social network interactions across sub-forums with different types of knowledge, as well as examined the range and depth of knowledge that users share across these sub-forums. The Enron study, on the other hand, investigated how social network structure could affect the expertise searching process in organizational communication networks using simulations and social network analysis. Based on findings in these studies, a novel expertise sharing system, QuME, was proposed and developed.
This thesis provides a network theoretical foundation for the analysis and design of knowledge sharing communities. It explores new opportunities and challenges that arise in online social interaction environments, which are becoming increasingly ubiquitous and important. This work also has direct implications for practitioners. The ability to add the level of expertise would be a major step forward for expertise finding systems, and would likely open up a range of new application possibilities.PhDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58450/1/junzh_1.pd
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
- …
