1,720,970 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Variations on the Author
Full text link“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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
On graph algorithms for large-scale graphs
Full text linkDie Anforderungen an Algorithmen hat sich in den letzten Jahren grundlegend geändert. Die Datengröße der zu verarbeitenden Daten wächst schneller als die zur Verfügung stehende Rechengeschwindigkeit. Daher sind neue Algorithmen auf sehr großen Graphen wie z.B. soziale Netzwerke, Computernetzwerke oder Zustandsübergangsgraphen entwickelt worden, um das Problem der immer größer werdenden Daten zu bewältigen. Diese Arbeit beschäftigt sich mit zwei Herangehensweisen für dieses Problem.
Implizite Algorithmen benutzten eine verlustfreie Kompression der Daten, um die Datengröße zu reduzieren, und arbeiten direkt mit den komprimierten Daten, um Optimierungsprobleme zu lösen. Graphen werden hier anhand der charakteristischen Funktion der Kantenmenge dargestellt, welche mit Hilfe von Ordered Binary Decision Diagrams (OBDDs) – eine bekannte Datenstruktur für Boolesche Funktionen - repräsentiert werden können. Wir entwickeln in dieser Arbeit neue Techniken, um die OBDD-Größe von Graphen zu bestimmen, und wenden diese Technik für mehrere Klassen von Graphen an und erhalten damit (fast) optimale Schranken für die OBDD-Größen. Kleine Eingabe-OBDDs sind essenziell für eine schnelle Verarbeitung, aber wir brauchen auch Algorithmen, die große Zwischenergebnisse während der Ausführung vermeiden. Hierfür entwickeln wir Algorithmen für bestimme Graphklassen, die die Kodierung der Knoten ausnutzt, die wir für die Resultate der OBDD-Größe benutzt haben. Zusätzlich legen wir die Grundlage für die Betrachtung von randomisierten OBDD-basierten Algorithmen, indem wir untersuchen, welche Art von Zufall wir hier verwenden und wie wir damit Algorithmen entwerfen können. Im Zuge dessen geben wir zwei randomisierte Algorithmen an, die ihre entsprechenden deterministischen Algorithmen in einer experimentellen Auswertung überlegen sind.
Datenstromalgoritmen sind eine weitere Möglichkeit für die Bearbeitung von großen Graphen. In diesem Modell wird der Graph anhand eines Datenstroms von Kanteneinfügungen repräsentiert und den Algorithmen steht nur eine begrenzte Menge von Speicher zur Verfügung. Lösungen für Graphoptimierungsprobleme benötigen häufig eine lineare Größe bzgl. der Anzahl der Knoten, was eine triviale untere Schranke für die Streamingalgorithmen für diese Probleme impliziert. Die Berechnung eines Matching ist so ein Beispiel, was aber in letzter Zeit viel Aufmerksamkeit in der Streaming-Community auf sich gezogen hat. Ein Matching ist eine Menge von Kanten, so dass keine zwei Kanten einen gemeinsamen Knoten besitzen. Wenn wir nur an der Größe oder dem Gewicht (im Falle von gewichteten Graphen) eines Matching interessiert sind, ist es mögliche diese lineare untere Schranke zu durchbrechen. Wir konzentrieren uns in dieser Arbeit auf dynamische Datenströme, wo auch Kanten gelöscht werden können. Wir reduzieren das Problem, einen Schätzer für ein gewichtsoptimales Matching zu finden, auf das Problem, die Größe von Matchings zu approximieren, wobei wir einen kleinen Verlust bzgl. der Approximationsgüte in Kauf nehmen müssen. Außerdem präsentieren wir den ersten dynamischen Streamingalgorithmus, der die Größe von Matchings in lokal spärlichen Graphen approximiert. Für kleine Approximationsfaktoren zeigen wir eine untere Schranke für den Platzbedarf von Streamingalgorithmen, die die Matchinggröße approximieren.The algorithmic challenges have changed in the last decade due to the rapid growth of the
data set sizes that need to be processed. New types of algorithms on large graphs like social
graphs, computer networks, or state transition graphs have emerged to overcome the problem of ever-increasing data sets. In this thesis, we investigate two approaches to this problem.
Implicit algorithms utilize lossless compression of data to reduce the size and to directly
work on this compressed representation to solve optimization problems. In the case of graphs
we are dealing with the characteristic function of the edge set which can be represented
by Ordered Binary Decision Diagrams (OBDDs), a well-known data structure for Boolean
functions. We develop a new technique to prove upper and lower bounds on the size of OBDDs representing graphs and apply this technique to several graph classes to obtain (almost) optimal bounds. A small input OBDD size is absolutely essential for dealing with large graphs but we also need algorithms that avoid large intermediate results during the computation. For this purpose, we design algorithms for a specific graph class that exploit the encoding of the nodes that we use for the results on the OBDD sizes. In addition, we lay the foundation on the theory of randomization in OBDD-based algorithms by investigating what kind of randomness is feasible and how to design algorithms with it. As a result, we present two randomized algorithms that outperform known deterministic algorithms on many input instances.
Streaming algorithms are another approach for dealing with large graphs. In this model, the
graph is presented one-by-one in a stream of edge insertions or deletions and the algorithms
are permitted to use only a limited amount of memory. Often, the solution to an optimization
problem on graphs can require up to a linear amount of space with respect to the number of
nodes, resulting in a trivial lower bound for the space requirement of any streaming algorithm
for those problems. Computing a matching, i. e., a subset of edges where no two edges are
incident to a common node, is an example which has recently attracted a lot of attention in
the streaming setting. If we are interested in the size (or weight in case of weighted graphs)
of a matching, it is possible to break this linear bound. We focus on so-called dynamic graph
streams where edges can be inserted and deleted and reduce the problem of estimating the
weight of a matching to the problem of estimating the size of a maximum matching with a
small loss in the approximation factor. In addition, we present the first dynamic graph stream
algorithm for estimating the size of a matching in graphs which are locally sparse. On the
negative side, we prove a space lower bound of streaming algorithms that estimate the size of
a maximum matching with a small approximation factor
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Information Complexity and Data Stream Algorithms for Basic Problems
Full text linkData stream algorithms obtain their input as a stream of data elements that have to be processed
immediately as they arrive using only a very limited amount of memory. They solve a
new class of algorithmic problems that emerged recently with the growing importance of computer
networks and the ever-increasing size of the data sets that are processed algorithmically.
In this thesis data stream algorithms for basic problems under extreme space restrictions are
developed, namely counting and random sampling. Then we apply these algorithms to improve
the space complexity of the celebrated data stream algorithm for the computation of
frequency moments by Alon, Matias, and Szegedy for very long data streams.
Lower bounds on the space complexity of data stream algorithms are usually proved
by using communication complexity arguments. Information complexity is a related field
that applies Shannon's information theory to obtain lower bounds on the communication
complexity of functions. The development of information complexity is closely linked to the
recent interest in data stream algorithms since important parts of this theory have been
developed to prove a lower bound on the space complexity of data stream algorithms for
the frequency moments. In this thesis we prove an optimal lower bound on the multi-party
information complexity of the disjointness function, the underlying communication problem
in the proof of the lower bound on the space complexity of data stream algorithms for the
frequency moments. Additionally, we generalize and simplify known lower bounds on the
one-way communication complexity of the index function by using information complexity
and we present the first attempt to apply information complexity to multi-party one-way
protocols in the number on the forehead model by Chandra, Furst, and Lipton
Algorithmik und Komplexität OBDD-repräsentierter Graphen
Full text linkOrdered Binary Decision Diagrams (OBDDs) werden in vielen praktischen Anwendungsgebieten erfolgreich als Datenstruktur zur kompakten Repräsentation boolescher Funktionen eingesetzt. Auch sehr große Graphen werden in Bereichen wie CAD und Model Checking oft implizit durch boolesche Funktionen und OBDDs dargestellt. Diese Dissertation behandelt grundlegende graphtheoretische Probleme auf OBDD-repräsentierten Graphen und lotet die
Möglichkeiten entsprechender OBDD-basierter Algorithmen aus. Zum einen werden neue Algorithmen vorgestellt und ihre Eigenschaften im Hinblick auf das Entwurfsziel sublinearer Heuristiken analysiert. Zum anderen werden Grenzen des Ansatzes durch komplexitätstheoretische Härteresultate und konkrete untere Schranken aufgezeigt
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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