56,406 research outputs found
GraphMaps: Browsing Large Graphs as Interactive Maps
Algorithms for laying out large graphs have seen significant progress in the
past decade. However, browsing large graphs remains a challenge. Rendering
thousands of graphical elements at once often results in a cluttered image, and
navigating these elements naively can cause disorientation. To address this
challenge we propose a method called GraphMaps, mimicking the browsing
experience of online geographic maps.
GraphMaps creates a sequence of layers, where each layer refines the previous
one. During graph browsing, GraphMaps chooses the layer corresponding to the
zoom level, and renders only those entities of the layer that intersect the
current viewport. The result is that, regardless of the graph size, the number
of entities rendered at each view does not exceed a predefined threshold, yet
all graph elements can be explored by the standard zoom and pan operations.
GraphMaps preprocesses a graph in such a way that during browsing, the
geometry of the entities is stable, and the viewer is responsive. Our case
studies indicate that GraphMaps is useful in gaining an overview of a large
graph, and also in exploring a graph on a finer level of detail.Comment: submitted to GD 201
Fast Multi-Scale Community Detection based on Local Criteria within a Multi-Threaded Algorithm
Many systems can be described using graphs, or networks. Detecting
communities in these networks can provide information about the underlying
structure and functioning of the original systems. Yet this detection is a
complex task and a large amount of work was dedicated to it in the past decade.
One important feature is that communities can be found at several scales, or
levels of resolution, indicating several levels of organisations. Therefore
solutions to the community structure may not be unique. Also networks tend to
be large and hence require efficient processing. In this work, we present a new
algorithm for the fast detection of communities across scales using a local
criterion. We exploit the local aspect of the criterion to enable parallel
computation and improve the algorithm's efficiency further. The algorithm is
tested against large generated multi-scale networks and experiments demonstrate
its efficiency and accuracy.Comment: arXiv admin note: text overlap with arXiv:1204.100
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