18,474 research outputs found
Fast filtering and animation of large dynamic networks
Detecting and visualizing what are the most relevant changes in an evolving
network is an open challenge in several domains. We present a fast algorithm
that filters subsets of the strongest nodes and edges representing an evolving
weighted graph and visualize it by either creating a movie, or by streaming it
to an interactive network visualization tool. The algorithm is an approximation
of exponential sliding time-window that scales linearly with the number of
interactions. We compare the algorithm against rectangular and exponential
sliding time-window methods. Our network filtering algorithm: i) captures
persistent trends in the structure of dynamic weighted networks, ii) smoothens
transitions between the snapshots of dynamic network, and iii) uses limited
memory and processor time. The algorithm is publicly available as open-source
software.Comment: 6 figures, 2 table
The Open Graph Archive: A Community-Driven Effort
In order to evaluate, compare, and tune graph algorithms, experiments on well
designed benchmark sets have to be performed. Together with the goal of
reproducibility of experimental results, this creates a demand for a public
archive to gather and store graph instances. Such an archive would ideally
allow annotation of instances or sets of graphs with additional information
like graph properties and references to the respective experiments and results.
Here we examine the requirements, and introduce a new community project with
the aim of producing an easily accessible library of graphs. Through successful
community involvement, it is expected that the archive will contain a
representative selection of both real-world and generated graph instances,
covering significant application areas as well as interesting classes of
graphs.Comment: 10 page
GraphCombEx: A Software Tool for Exploration of Combinatorial Optimisation Properties of Large Graphs
We present a prototype of a software tool for exploration of multiple
combinatorial optimisation problems in large real-world and synthetic complex
networks. Our tool, called GraphCombEx (an acronym of Graph Combinatorial
Explorer), provides a unified framework for scalable computation and
presentation of high-quality suboptimal solutions and bounds for a number of
widely studied combinatorial optimisation problems. Efficient representation
and applicability to large-scale graphs and complex networks are particularly
considered in its design. The problems currently supported include maximum
clique, graph colouring, maximum independent set, minimum vertex clique
covering, minimum dominating set, as well as the longest simple cycle problem.
Suboptimal solutions and intervals for optimal objective values are estimated
using scalable heuristics. The tool is designed with extensibility in mind,
with the view of further problems and both new fast and high-performance
heuristics to be added in the future. GraphCombEx has already been successfully
used as a support tool in a number of recent research studies using
combinatorial optimisation to analyse complex networks, indicating its promise
as a research software tool
Computing Storyline Visualizations with Few Block Crossings
Storyline visualizations show the structure of a story, by depicting the
interactions of the characters over time. Each character is represented by an
x-monotone curve from left to right, and a meeting is represented by having the
curves of the participating characters run close together for some time. There
have been various approaches to drawing storyline visualizations in an
automated way. In order to keep the visual complexity low, rather than
minimizing pairwise crossings of curves, we count block crossings, that is,
pairs of intersecting bundles of lines.
Partly inspired by the ILP-based approach of Gronemann et al. [GD 2016] for
minimizing the number of pairwise crossings, we model the problem as a
satisfiability problem (since the straightforward ILP formulation becomes more
complicated and harder to solve). Having restricted ourselves to a decision
problem, we can apply powerful SAT solvers to find optimal drawings in
reasonable time. We compare this SAT-based approach with two exact algorithms
for block crossing minimization, using both the benchmark instances of
Gronemann et al. and random instances. We show that the SAT approach is
suitable for real-world instances and identify cases where the other algorithms
are preferable.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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