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Field Methods
In this article, the authors explore the use of graph layout algorithms for visualizing proximity matrices such as those obtained in cultural domain analysis. Traditionally, multidimensional scaling has been used for this ...
Institute of Mathematical Statistics Lecture Notes - Monograph Series, 2000
In this paper we explore the relationship between multivariate data analysis and techniques for graph drawing or graph layout. Although both classes of techniques were created for quite different purposes, we find many common principles and implementations. We start with a discussion of the data analysis techniques, in particular multiple correspondence analysis, multidimensional scaling, parallel coordinate plotting, and seriation. We then discuss parallels in the graph layout literature. Categories of second variable FIGURE 1. The multivariable graph of a toy example 1 A bipartite graph is a 2-layered graph, where edges only go from one layer to the other layer.
Visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context. Context, in its turn, is given by attributes associated with graph elements, such as individual nodes, edges, and groups of edges, as well as by the nature of the connections between individuals. In most systems, attributes of individuals and communities are not taken into consideration during graph layout, except to derive weights for force-based placement strategies. This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings. These properties are employed to layout nodes on the plane via multidimensional projection techniques. For the attribute mapping, we show that node proximity in the layout corresponds to similarity in attribute, leading to easiness in locating similar groups of nodes. The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm, reaching a meaningful layout in one pass. When a force algorithm is then applied to this initial mapping, the final layout presents better properties than conventional force-based approaches. Numerical evaluations show a number of advantages of pre-mapping points via projections. User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone. In order to allow better space usage for complex networks, a graph mapping on the surface of a sphere is also implemented.
Information Visualization, 2013
Many algorithms for graph layout have been devised over the last 30 years spanning both the graph drawing and information visualisation communities. This article first reviews the advances made in the field of graph drawing that have then often been applied by the information visualisation community. There then follows a discussion of a range of techniques developed specifically for graph visualisations. Graph drawing algorithms are categorised into the followings approaches: forcedirected layouts, the use of dimension reduction in graph layout and computational improvements including multi-level techniques. Methods developed specifically for graph visualisation often make use of node-attributes and are categorised based on whether the attributes are used to introduce constraints to the layout, provide a clustered view or used to define an explicit representation in 2D space. The similarities and distinctions between these techniques are examined and the aim is to provide a detailed assessment of currently available graph layout techniques, specifically how they can be used by visualisation practitioners and to motivate further research in the area.
Bulletin of Electrical Engineering and Informatics, 2025
This research aims to produce a new method called pasca-multidimensional scaling (pasca-MDS) by modifying the multidimensional scaling (MDS) method, the developed model comes as a solution to overcome the problem of data complexity by reducing its description dimension without losing important information. This model, offers an innovative approach in dealing with these problems. Pasca-MDS not only focuses on reducing the dimensionality of data, but also retains the essence of relevant information from each data point. As such, it allows for easier and more efficient analysis without compromising the accuracy of the information conveyed. The main advantage of pasca-MDS lies in its ability to produce simpler visual representations while maintaining the original structure of complex data. This provides clarity and ease in understanding the patterns or relationships hidden within. By using adjustment techniques after the MDS process, this model can provide more optimized results. This process allows the adjustment of data points to achieve a better representation in a lower dimensional space, resulting in a more intuitive and easy-to-understand interpretation. The developed distance formula has the ability to minimize stress compared to other distance formulas in MDS space, with the aim of improving the accuracy of high-dimensional data visualization.
Applied Psychological Measurement, 1992
A number of model-based scaling methods have been developed that apply to asymmetric proximity matrices. A flexible data analysis approach is pro posed that combines two psychometric procedures— seriation and multidimensional scaling (MDS). The method uses seriation to define an empirical order ing of the stimuli, and then uses MDS to scale the two separate triangles of the proximity matrix defined by this ordering. The MDS solution con tains directed distances, which define an "extra" dimension that would not otherwise be portrayed, because the dimension comes from relations between the two triangles rather than within triangles. The method is particularly appropriate for the analysis of proximities containing temporal information. A major difficulty is the computa tional intensity of existing seriation algorithms, which is handled by defining a nonmetric seriation algorithm that requires only one complete itera tion. The procedure is illustrated using a matrix of co-cita...
Applied Soft Computing, 2016
Many networks exhibit small-world properties. The structure of a small-world network is characterized by short average path lengths and high clustering coefficients. Few graph layout methods capture this structure well which limits their effectiveness and the utility of the visualization itself. Here we present an extension to our novel graphTPP layout method for laying out small-world networks using only their topological properties rather than their node attributes. The Watts-Strogatz model is used to generate a variety of graphs with a small-world network structure. Community detection algorithms are used to generate six different clusterings of the data. These clusterings, the adjacency matrix and edgelist are loaded into graphTPP and, through user interaction combined with linear projections of the adjacency matrix, graphTPP is able to produce a layout which visually separates these clusters. These layouts are compared to the layouts of two force-based techniques. graphTPP is able to clearly separate each of the communities into a spatially distinct area and the edge relationships between the clusters show the strength of their relationship. As a secondary contribution, an edge-grouping algorithm for graphTPP is demonstrated as a means to reduce visual clutter in the layout and reinforce the display of the strength of the relationship between two communities.
Exploring Geovisualization, 2005
2011
Abstract Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar.
PloS one 9(6), 2014
Gephi is a network visualization software used in various disciplines (social network analysis, biology, genomics...). One of its key features is the ability to display the spatialization process, aiming at transforming the network into a map, and ForceAtlas2 is its default layout algorithm. The latter is developed by the Gephi team as an all-around solution to Gephi users' typical networks (scale-free, 10 to 10,000 nodes). We present here for the first time its functioning and settings. ForceAtlas2 is a force-directed layout close to other algorithms used for network spatialization. We do not claim a theoretical advance but an attempt to integrate different techniques such as the Barnes Hut simulation, degree-dependent repulsive force, and local and global adaptive temperatures. It is designed for the Gephi user experience (it is a continuous algorithm), and we explain which constraints it implies. The algorithm benefits from much feedback and is developed in order to provide m...
7th International Symposium of Hungarian Researchers on Computational Intelligence; pp. 483-494, 2006
The basis of the presented methods for the visualization and clustering of graphs is a novel similarity and distance metric, and the matrix describing the similarity of the nodes in the graph. This matrix represents the type of connections between the nodes in the graph in a compact form, thus it provides a very good starting point for both the clustering and visualization algorithms. Hence visualization is done with the MDS (Multidimensional Scaling) dimensionality reduction technique obtaining the spectral decomposition of this matrix, while the partitioning is based on the results of this step generating a hierarchical representation. A detailed example is shown to justify the capability of the described algorithms for clustering and visualization of the link structure of Web sites.
ArXiv, 2017
Graph layout is the process of creating a visual representation of a graph through a node-link diagram. Node-attribute graphs have additional data stored on the nodes which describe certain properties of the nodes called attributes. Typical force-directed representations often produce hairball-like structures that neither aid in understanding the graph's topology nor the relationship to its attributes. The aim of this research was to investigate the use of node-attributes for graph layout in order to improve the analysis process and to give further insight into the graph over purely topological layouts. In this article we present graphTPP, a graph based extension to targeted projection pursuit (TPP) --- an interactive, linear, dimension reduction technique --- as a method for graph layout and subsequent further analysis. TPP allows users to control the projection and is optimised for clustering. Three case studies were conducted in the areas of influence graphs, network security...
2018
"Near things are more similar than more distant things" states Tobler's first law of geography. This seems obvious and is part to much cognitive research into the perception of the environment. The statement's validity for assessments of geographical nearness purely from map symbols has yet to be ascertained. This paper considers this issue through a theoretical framework grounded in Gestalt concepts, behavioral ecological psychology and information psychology. It sets out to consider how influential experience or training may be on the association of graphical proximity with geographical nearness. A pilot study presents some initial findings. The findings regarding the influence of experience or training are ambiguous, but point to the rapid acquisition of affordances in the survey instruments as another factor for future research.
International Journal of Computer Applications, 2013
Metrovis is an extensible graph drawing package with Silverlight technology. This package is a web base tool to present the octilinear layout of a graph. It provides an application programming interface (API) on top of which Web-based applications that need to visualize any entities in terms of graphs. This layout has got many application such as representation of thesis map, designing the web pages. Metrovis, could be enhanced in any platform, and it's compatible by any algorithm. To illustrate its application, it has been performed with the real metro maps.
IFIP International Federation for Information Processing, 2006
This paper introduces an automatic procedure to assist on the interpretation of a large dataset when a similarity metric is available. We propose a visualization approach based on a graph layout methodology that uses a Quadratic Assignment Problem (QAP) formulation. The methodology is presented using as testbed a time series dataset of the Standard & Poor's 100, one the leading stock market indicators in the United States. A weighted graph is created with the stocks represented by the nodes and the edges' weights are related to the correlation between the stocks' time series. A heuristic for clustering is then proposed; it is based on the graph partition into disconnected subgraphs allowing the identification of clusters of highly-correlated stocks. The final layout corresponds well with the perceived market notion of the different industrial sectors. We compare the output of this procedure with a traditional dendogram approach of hierarchical clustering.
2013 5th International Conference on Intelligent Networking and Collaborative Systems, 2013
Visualization is an important part of Network Analysis. It helps to find features of the network that are not easily identifiable. In this paper we present our approach to the visualization of weighted networks based on Sammon's projection. The network may be seen as a set of data points in the space induced by the incidence relation or as a symmetric matrix of vertex distances. We propose several methods for construction of the input for the Sammon's projection and discuss the effect of the particular methods on the final layout. Results are illustrated using several networks in the 2D layout. Presented experiments use the well-known Karate club network and weighted coauthors network based on the DBLP database.
Serbian Journal of Engineering Management, 2021
Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be n...
Journal of Visualization, 2016
In a variety of research and application areas, graphs are an important structure for data modeling and analysis. While graph properties can have a crucial influence on the performance of graph algorithms, and thus on the outcome of experiments, often only basic analysis of the graphs under investigation in an experimental evaluation is performed and a few characteristics are reported in publications. We present Graph Landscape, a concept for the visual analysis of graph set properties. The Graph Landscape aims to support researchers to explore graphs and graph sets regarding their properties, to allow to select good experimental test sets, analyze newly generated sets, compare sets and assess the validity (or range) of experimental results and corresponding conclusions. Keywords Graphs Á Multidimensional data Á Visualization Á Analysis 1 Introduction Graphs are a versatile data structure that is used to model data in a variety of domains, including biology, security, social sciences, telecommunication, economics, and engineering. The resulting graphs and their properties facilitate the analysis of the data, e.g., in biology to find out the underlying principles of biological processes, or in social sciences to discover leaders and dynamic group behavior. Many online databases and collections of graphs are available that support such network analysis (KEGG Kyoto
ABSTRACT Cartographers have long faced the problem of how to create maps that have many data values available for each observation, for example, when analyzing causes and consequences of poverty at the national scale, we might have hundreds of relevant variables available for each country. This problem is especially daunting when the relationships between the different variables are not known clearly.
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