10,123 research outputs found
SWING: A system for visualizing web graphs
A Web graph refers to the graph that is used to represent relationships between Web pages in cyberspace, where a node represents a URL and an edge indicates a link between two URLs. A Web graph is a very huge graph as growing with cyberspace. This paper presents a pipeline for extracting web information from cyberspace to a web graph and layout techniques for making the web graph more readable. As the size of computer screen is limited, only a small part of the Web graph can be displayed. Several layout techniques should be adapted and combined effectively for web graph visualization. The visualization process incorporates graph drawing algorithms, layout adjustment methods, as well as filtering and clustering methods in order to decide which part of the Web graph should be displayed and how to display it based on the user's focus in navigation
Augmenting citation chain aggregation with article maps
Presentation slides available at: https://www.gesis.org/fileadmin/upload/kmir2014/paper4_slides.pdfThis paper presents Voyster, an experimental system that combines citation chain aggregation (CCA) and spatial-semantic maps to support citation search. CCA uses a three-list view to represent the citation network surrounding a ‘pearl’ of known relevant articles, whereby cited and citing articles are ranked according to number of pearl relations. As the pearl grows, this overlap score provides an effective proxy for relevance. However, when the pearl is small or multi-faceted overlap ranking provides poor discrimination. To address this problem we augment the lists with a visual map, wherein articles are organized according to their content similarity. We demonstrate how the article map can help the user to make relevant choices during the early stages of the search pro-cess and also provide useful insights into the thematic structure of the local citation network
Large Graph Analysis in the GMine System
Current applications have produced graphs on the order of hundreds of
thousands of nodes and millions of edges. To take advantage of such graphs, one
must be able to find patterns, outliers and communities. These tasks are better
performed in an interactive environment, where human expertise can guide the
process. For large graphs, though, there are some challenges: the excessive
processing requirements are prohibitive, and drawing hundred-thousand nodes
results in cluttered images hard to comprehend. To cope with these problems, we
propose an innovative framework suited for any kind of tree-like graph visual
design. GMine integrates (a) a representation for graphs organized as
hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b)
a graph summarization methodology - CEPS. Our graph representation deals with
the problem of tracing the connection aspects of a graph hierarchy with sub
linear complexity, allowing one to grasp the neighborhood of a single node or
of a group of nodes in a single click. As a proof of concept, the visual
environment of GMine is instantiated as a system in which large graphs can be
investigated globally and locally
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