299 research outputs found
Tag-Cloud Drawing: Algorithms for Cloud Visualization
Tag clouds provide an aggregate of tag-usage statistics. They are typically
sent as in-line HTML to browsers. However, display mechanisms suited for
ordinary text are not ideal for tags, because font sizes may vary widely on a
line. As well, the typical layout does not account for relationships that may
be known between tags. This paper presents models and algorithms to improve the
display of tag clouds that consist of in-line HTML, as well as algorithms that
use nested tables to achieve a more general 2-dimensional layout in which tag
relationships are considered. The first algorithms leverage prior work in
typesetting and rectangle packing, whereas the second group of algorithms
leverage prior work in Electronic Design Automation. Experiments show our
algorithms can be efficiently implemented and perform well.Comment: To appear in proceedings of Tagging and Metadata for Social
Information Organization (WWW 2007
Mixed coordinate Node link Visualization for Co_authorship Hypergraph Networks
We present an algorithmic technique for visualizing the co-authorship
networks and other networks modeled with hypergraphs (set systems). As more
than two researchers can co-author a paper, a direct representation of the
interaction of researchers through their joint works cannot be adequately
modeled with direct links between the author-nodes. A hypergraph representation
of a co-authorship network treats researchers/authors as nodes and papers as
hyperedges (sets of authors). The visualization algorithm that we propose is
based on one of the well-studied approaches representing both authors and
papers as nodes of different classes. Our approach resembles some known ones
like anchored maps but introduces some special techniques for optimizing the
vertex positioning. The algorithm involves both continuous (force-directed)
optimization and discrete optimization for determining the node coordinates.
Moreover, one of the novelties of this work is classifying nodes and links
using different colors. This usage has a meaningful purpose that helps the
viewer to obtain valuable information from the visualization and increases the
readability of the layout. The algorithm is tuned to enable the viewer to
answer questions specific to co-authorship network studies.Comment: 10 pages, 3 figures, 1 tabl
3D IC optimal layout design. A parallel and distributed topological approach
The task of 3D ICs layout design involves the assembly of millions of
components taking into account many different requirements and constraints such
as topological, wiring or manufacturability ones. It is a NP-hard problem that
requires new non-deterministic and heuristic algorithms. Considering the time
complexity, the commonly applied Fiduccia-Mattheyses partitioning algorithm is
superior to any other local search method. Nevertheless, it can often miss to
reach a quasi-optimal solution in 3D spaces. The presented approach uses an
original 3D layout graph partitioning heuristics implemented with use of the
extremal optimization method. The goal is to minimize the total wire-length in
the chip. In order to improve the time complexity a parallel and distributed
Java implementation is applied. Inside one Java Virtual Machine separate
optimization algorithms are executed by independent threads. The work may also
be shared among different machines by means of The Java Remote Method
Invocation system.Comment: 26 pages, 9 figure
MetroSets: Visualizing Sets as Metro Maps
We propose MetroSets, a new, flexible online tool for visualizing set systems
using the metro map metaphor. We model a given set system as a hypergraph
, consisting of a set of vertices and a set
, which contains subsets of called hyperedges. Our system then
computes a metro map representation of , where each hyperedge
in corresponds to a metro line and each vertex corresponds to a
metro station. Vertices that appear in two or more hyperedges are drawn as
interchanges in the metro map, connecting the different sets. MetroSets is
based on a modular 4-step pipeline which constructs and optimizes a path-based
hypergraph support, which is then drawn and schematized using metro map layout
algorithms. We propose and implement multiple algorithms for each step of the
MetroSet pipeline and provide a functional prototype with \new{easy-to-use
preset configurations.} % many real-world datasets. Furthermore, \new{using
several real-world datasets}, we perform an extensive quantitative evaluation
of the impact of different pipeline stages on desirable properties of the
generated maps, such as octolinearity, monotonicity, and edge uniformity.Comment: 19 pages; accepted for IEEE INFOVIS 2020; for associated live system,
see http://metrosets.ac.tuwien.ac.a
Dynamic Euler Diagram Drawing
In this paper we describe a method to lay out a graph enhanced Euler diagram so that it looks similar to a previously drawn graph enhanced Euler diagram. This task is non-trivial when the underlying structures of the diagrams differ. In particular, if a structural change is made to an existing drawn diagram, our work enables the presentation of the new diagram with minor disruption to the user's mental map. As the new diagram can be generated from an abstract representation, its initial embedding may be very different from that of the original. We have developed comparison measures for Euler diagrams, integrated into a multicriteria optimizer, and applied a force model for associated graphs that attempts to move nodes towards their positions in the original layout. To further enhance the usability of the system, the transition between diagrams can be animated
Inferring community structure in attributed hypergraphs using stochastic block models
Hypergraphs are a representation of complex systems involving interactions
among more than two entities and allow to investigation of higher-order
structure and dynamics in real-world complex systems. Community structure is a
common property observed in empirical networks in various domains. Stochastic
block models have been employed to investigate community structure in networks.
Node attribute data, often accompanying network data, has been found to
potentially enhance the learning of community structure in dyadic networks. In
this study, we develop a statistical framework that incorporates node attribute
data into the learning of community structure in a hypergraph, employing a
stochastic block model. We demonstrate that our model, which we refer to as
HyperNEO, enhances the learning of community structure in synthetic and
empirical hypergraphs when node attributes are sufficiently associated with the
communities. Furthermore, we found that applying a dimensionality reduction
method, UMAP, to the learned representations obtained using stochastic block
models, including our model, maps nodes into a two-dimensional vector space
while largely preserving community structure in empirical hypergraphs. We
expect that our framework will broaden the investigation and understanding of
higher-order community structure in real-world complex systems.Comment: 28 pages, 11 figures, 8 table
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
The State-of-the-Art of Set Visualization
Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net
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