1,721,028 research outputs found
Large graph visualizations using a distributed computing platform
Big Data analytics is recognized as one of the major issues in our current information society, and raises several challenges and opportunities in many fields, including economy and finance, e-commerce, public health and administration, national security, and scientific research. The use of visualization techniques to make sense of large volumes of information is an essential ingredient, especially for the analysis of complex interrelated data, which are represented as graphs. The growing availability of powerful and inexpensive cloud computing services naturally motivates the study of distributed graph visualization algorithms, able to scale to the size of large graphs. We study the problem of designing a distributed visualization algorithm that must be simple to implement and whose computing infrastructure does not require major hardware or software investments. We design, implement, and experiment a force-directed algorithm in Giraph, a popular open source framework for distributed computing, based on a vertex-centric design paradigm. The algorithm is tested both on real and artificial graphs with up to one million edges. The experiments show the scalability and effectiveness of our technique when compared to a centralized implementation of the same force-directed model. Graphs with about one million edges can be drawn in a few minutes, by spending about 1 USD per drawing with a cloud computing infrastructure of Amazon
Profiling distributed graph processing systems through visual analytics
Analyzing large-scale graphs provides valuable insights in different application scenarios, including social networking, crime detection, content ranking, and recommendations. While many graph processing systems working on top of distributed infrastructures have been proposed to deal with big graphs, the task of profiling their massive computations remains time consuming and error-prone. This paper presents GiViP, a visual profiler for distributed graph processing systems based on a Pregel-like computation model. GiViP captures the huge amount of messages exchanged throughout a computation and provides a powerful user interface for the visual analysis of the collected data. We discuss the effectiveness of our approach and show how to take advantage of GiViP to detect anomalies related to the computation and to the infrastructure, such as slow computing units, anomalous message patterns, unbalanced graph partitions, and links with high latency
A Million Edge Drawing for a Fistful of Dollars
In this paper we study the problem of designing a graph drawing algorithm for large graphs. The algorithm must be simple to implement and the computing infrastructure must not require major hardware or software investments. We report about the experimental analysis of a simple implementation of a spring embedder in Giraph, a vertex-centric open source framework for distributed computing. The algorithm is tested on real graphs of up to 1 million edges by using a cheap PaaS (Platform as a Service) infrastructure of Amazon. We can afford drawing graphs with about one million edges in about 8 min, by spending less than 1 USD per drawing for the cloud computing infrastructure
A Distributed Multilevel Force-Directed Algorithm
The wide availability of powerful and inexpensive cloud computing services naturally motivates the study of distributed graph layout algorithms, able to scale to very large graphs. Nowadays, to process Big Data, companies are increasingly relying on PaaS infrastructures rather than buying and maintaining complex and expensive hardware. So far, only a few examples of basic force-directed algorithms that work in a distributed environment have been described. Instead, the design of a distributed multilevel force-directed algorithm is a much more challenging task, not yet addressed. We present the first multilevel force-directed algorithm based on a distributed vertex-centric paradigm, and its implementation on Giraph, a popular platform for distributed graph algorithms. Experiments show the effectiveness and the scalability of the approach. Using an inexpensive cloud computing service of Amazon, we draw graphs with ten million edges in about 60 min
Going Beyond Counting First Authors in Author Co-citation Analysis
The 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
“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
We 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
Dispelling the Myths Behind First-author Citation Counts
We 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
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