65 research outputs found
Node-attribute graph layout for small-world networks
Small-world networks are a very commonly occurring type of graph in the real-world, which exhibit a clustered structure that is not well represented by current graph layout algorithms. In many cases we also have information about the nodes in such graphs, which are typically depicted on the graph as node colour, shape or size. Here we demonstrate that these attributes can instead be used to layout the graph in high-dimensional data space. Then using a dimension reduction technique, targeted projection pursuit, the graph layout can be optimised for displaying clustering. The technique out-performs force-directed layout methods in cluster separation when applied to a sample, artificially generated, small-world network
NodeTrix: Hybrid Representation for Analyzing Social Networks
The need to visualize large social networks is growing as hardware
capabilities make analyzing large networks feasible and many new data sets
become available. Unfortunately, the visualizations in existing systems do not
satisfactorily answer the basic dilemma of being readable both for the global
structure of the network and also for detailed analysis of local communities.
To address this problem, we present NodeTrix, a hybrid representation for
networks that combines the advantages of two traditional representations:
node-link diagrams are used to show the global structure of a network, while
arbitrary portions of the network can be shown as adjacency matrices to better
support the analysis of communities. A key contribution is a set of interaction
techniques. These allow analysts to create a NodeTrix visualization by dragging
selections from either a node-link or a matrix, flexibly manipulate the
NodeTrix representation to explore the dataset, and create meaningful summary
visualizations of their findings. Finally, we present a case study applying
NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate
the capabilities of NodeTrix as both an exploration tool and an effective means
of communicating results
A General Introduction To Graph Visualization Techniques
Generally, a graph is an abstract data type used to represent relations among a given set of data entities. Graphs are used in numerous applications within the field of information visualization, such as VLSI (circuit schematics), state-transition diagrams, and social networks. The size and complexity of graphs easily reach dimensions at which the task of exploring and navigating gets crucial. Moreover, additional requirements have to be met in order to provide proper visualizations. In this context, many techniques already have been introduced. This survey aims to provide an introduction on graph visualization techniques helping the reader to gain a first insight into the most fundamental techniques. Furthermore, a brief introduction about navigation and interaction tools is provided
Multi-Perspective, Simultaneous Embedding
We describe MPSE: a Multi-Perspective Simultaneous Embedding method for
visualizing high-dimensional data, based on multiple pairwise distances between
the data points. Specifically, MPSE computes positions for the points in 3D and
provides different views into the data by means of 2D projections (planes) that
preserve each of the given distance matrices. We consider two versions of the
problem: fixed projections and variable projections. MPSE with fixed
projections takes as input a set of pairwise distance matrices defined on the
data points, along with the same number of projections and embeds the points in
3D so that the pairwise distances are preserved in the given projections. MPSE
with variable projections takes as input a set of pairwise distance matrices
and embeds the points in 3D while also computing the appropriate projections
that preserve the pairwise distances. The proposed approach can be useful in
multiple scenarios: from creating simultaneous embedding of multiple graphs on
the same set of vertices, to reconstructing a 3D object from multiple 2D
snapshots, to analyzing data from multiple points of view. We provide a
functional prototype of MPSE that is based on an adaptive and stochastic
generalization of multi-dimensional scaling to multiple distances and multiple
variable projections. We provide an extensive quantitative evaluation with
datasets of different sizes and using different number of projections, as well
as several examples that illustrate the quality of the resulting solutions
A user study on curved edges in graph visualization
Recently there has been increasing research interest in displaying graphs with curved edges to produce more readable visualizations. While there are several automatic techniques, little has been done to evaluate their effectiveness empirically. In this paper we present two experiments studying the impact of edge curvature on graph readability. The goal is to understand the advantages and disadvantages of using curved edges for common graph tasks compared to straight line segments, which are the conventional choice for showing edges in node-link diagrams. We included several edge variations: straight edges, edges with different curvature levels, and mixed straight and curved edges. During the experiments, participants were asked to complete network tasks including determination of connectivity, shortest path, node degree, and common neighbors. We also asked the participants to provide subjective ratings of the aesthetics of different edge types. The results show significant performance differences between the straight and curved edges and clear distinctions between variations of curved edges
Visual Encodings for Networks with Multiple Edge Types
This paper reports on a formal user study on visual encodings ofnetworks with multiple edge types in adjacency matrices. Our tasksand conditions were inspired by real problems in computationalbiology. We focus on encodings in adjacency matrices, selectingfour designs from a potentially huge design space of visual encodings.We then settle on three visual variables to evaluate in acrowdsourcing study with 159 participants: orientation, positionand colour. The best encodings were integrated into a visual analyticstool for inferring dynamic Bayesian networks and evaluated bycomputational biologists for additional evidence.We found that theencodings performed differently depending on the task, however,colour was found to help in all tasks except when trying to find theedge with the largest number of edge types. Orientation generallyoutperformed position in all of our tasks
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