103,512 research outputs found
Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs
The overview-driven visual analysis of large-scale dynamic graphs poses a
major challenge. We propose Multiscale Snapshots, a visual analytics approach
to analyze temporal summaries of dynamic graphs at multiple temporal scales.
First, we recursively generate temporal summaries to abstract overlapping
sequences of graphs into compact snapshots. Second, we apply graph embeddings
to the snapshots to learn low-dimensional representations of each sequence of
graphs to speed up specific analytical tasks (e.g., similarity search). Third,
we visualize the evolving data from a coarse to fine-granular snapshots to
semi-automatically analyze temporal states, trends, and outliers. The approach
enables to discover similar temporal summaries (e.g., recurring states),
reduces the temporal data to speed up automatic analysis, and to explore both
structural and temporal properties of a dynamic graph. We demonstrate the
usefulness of our approach by a quantitative evaluation and the application to
a real-world dataset.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to
appea
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
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
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