Performance comparisons between force-directed algorithms on structured data analysis

© 2017 IEEE. Evaluation on the performance of relevant force-directed algorithms is still a challenge, since layout quality is largely relying on personal judgement and/or methods' input parameters, and most aesthetics criteria conflict with each other. This study conducts the performance measurements of four algorithms in terms of seven commonly applied aesthetic criteria and demonstrates the experimental framework

SWING: A system for visualizing web graphs

A Web graph refers to the graph that is used to represent relationships between Web pages in cyberspace, where a node represents a URL and an edge indicates a link between two URLs. A Web graph is a very huge graph as growing with cyberspace. This paper presents a pipeline for extracting web information from cyberspace to a web graph and layout techniques for making the web graph more readable. As the size of computer screen is limited, only a small part of the Web graph can be displayed. Several layout techniques should be adapted and combined effectively for web graph visualization. The visualization process incorporates graph drawing algorithms, layout adjustment methods, as well as filtering and clustering methods in order to decide which part of the Web graph should be displayed and how to display it based on the user's focus in navigation

Drawing large weighted graphs using clustered force-directed algorithm

© 2014 IEEE. Clustered graph drawing is widely considered as a good method to overcome the scalability problem when visualizing large (or huge) graphs. Force-directed algorithm is a popular approach for laying graphs yet small to medium size datasets due to its slow convergence time. This paper proposes a new method which combines clustering and a force-directed algorithm, to reduce the computational complexity and time. It works by dividing a Long Convergence: LC into two Short Convergences: SC1, SC2, where SC1+SC2 < LC. We also apply our work on weighted graphs. Our experiments show that the new method improves the aesthetics in graph visualization by providing clearer views for connectivity and edge weights

Graph Layout Performance Comparisons of Force-Directed Algorithms

© 2018 Totem Publisher, Inc. All rights reserved. Due to force-directed algorithms’ capabilities of producing aesthetically pleasing graph layouts, which follow metrics for graph drawing aesthetics, these layouts have become the most common methods in the practical data visualization area. However, evaluating the performance of relevant algorithms remains a challenge, since graph layout quality is largely relying on aspects such as human intuition, personal judgment and methods’ pre-setting parameters. In addition, most aesthetics criteria of graph drawing conflict with each other. This study evaluated the performance measurements of four force-directed algorithms in terms of seven commonly applied aesthetic criteria based on practical raw data collected, and demonstrated the experimental framework. The early outcomes compared twenty final graph layouts and gave empirical evidences; the study may assist with future detailed force-directed algorithms selection based on users’ specific requirements

Fast convergence layout algorithm for drawing graphs in marching-graph

Marching-Graph is a new visualization that integrates the graph metaphor and the spatial metaphor into a single visualization. It provides users with highly interactive maps for accessing the logical structures of information that has the geographical attributes. Instead of presenting known facts onto maps, it provides a mechanism for users to visually analyze and seek unknown knowledge through effective human-map interaction and navigation across different spaces. However, the traditional force-directed layout algorithms are very slow in reaching an equilibrium configuration of forces. They usually spend tens of seconds making the layout of a graph converge. Thus, those force-directed layout algorithms can not satisfy the requirement for drawing a sequence of graphs rapidly, while the users are quickly marching through the geographic regions. This paper proposes a fast convergence layout method that speeds up the interaction time while users are progressively exploring a sequence of graphs through a series of force-directed layouts in Marching-Graph. It essentially combines a radial tree drawing method and a force-directed graph drawing method to achieve the fast convergence of energy minimization

Visual Exploration System for Analyzing Trends in Annual Recruitment Using Time-varying Graphs

Annual recruitment data of new graduates are manually analyzed by human resources specialists (HR) in industries, which signifies the need to evaluate the recruitment strategy of HR specialists. Every year, different applicants send in job applications to companies. The relationships between applicants' attributes (e.g., English skill or academic credential) can be used to analyze the changes in recruitment trends across multiple years' data. However, most attributes are unnormalized and thus require thorough preprocessing. Such unnormalized data hinder the effective comparison of the relationship between applicants in the early stage of data analysis. Thus, a visual exploration system is highly needed to gain insight from the overview of the relationship between applicants across multiple years. In this study, we propose the Polarizing Attributes for Network Analysis of Correlation on Entities Association (Panacea) visualization system. The proposed system integrates a time-varying graph model and dynamic graph visualization for heterogeneous tabular data. Using this system, human resource specialists can interactively inspect the relationships between two attributes of prospective employees across multiple years. Further, we demonstrate the usability of Panacea with representative examples for finding hidden trends in real-world datasets and then describe HR specialists' feedback obtained throughout Panacea's development. The proposed Panacea system enables HR specialists to visually explore the annual recruitment of new graduates

Doctor of Philosophy

dissertationA broad range of applications capture dynamic data at an unprecedented scale. Independent of the application area, finding intuitive ways to understand the dynamic aspects of these increasingly large data sets remains an interesting and, to some extent, unsolved research problem. Generically, dynamic data sets can be described by some, often hierarchical, notion of feature of interest that exists at each moment in time, and those features evolve across time. Consequently, exploring the evolution of these features is considered to be one natural way of studying these data sets. Usually, this process entails the ability to: 1) define and extract features from each time step in the data set; 2) find their correspondences over time; and 3) analyze their evolution across time. However, due to the large data sizes, visualizing the evolution of features in a comprehensible manner and performing interactive changes are challenging. Furthermore, feature evolution details are often unmanageably large and complex, making it difficult to identify the temporal trends in the underlying data. Additionally, many existing approaches develop these components in a specialized and standalone manner, thus failing to address the general task of understanding feature evolution across time. This dissertation demonstrates that interactive exploration of feature evolution can be achieved in a non-domain-specific manner so that it can be applied across a wide variety of application domains. In particular, a novel generic visualization and analysis environment that couples a multiresolution unified spatiotemporal representation of features with progressive layout and visualization strategies for studying the feature evolution across time is introduced. This flexible framework enables on-the-fly changes to feature definitions, their correspondences, and other arbitrary attributes while providing an interactive view of the resulting feature evolution details. Furthermore, to reduce the visual complexity within the feature evolution details, several subselection-based and localized, per-feature parameter value-based strategies are also enabled. The utility and generality of this framework is demonstrated by using several large-scale dynamic data sets

Interactive visualisation tools for supporting taxonomists working practice.

The necessity for scientists and others to use consistent terminology has recently beenregarded as fundamental to advancing scientific research, particularly where data fromdisparate sources must be shared, compared or integrated. One area where there aresignificant difficulties with the quality of collected data is the field of taxonomicdescription. Taxonomic description lies at the heart of the classification of organismsand communication of ideas of biodiversity. As part of their working practice,taxonomists need to gather descriptive data about a number of specimens on aconsistent basis for individual projects. Collecting semantically well-defined structureddata could improve the clarity and comparability of such data. No tools howevercurrently exist to allow taxonomists to do so within their working practice.Ontologies are increasingly used to describe and define complex domain data. As a partof related research an ontology of descriptive terminology for controlling the storageand use of flowering plant description data was developed.This work has applied and extended model-based user interface developmentenvironments to utilise such an ontology for the automatic generation of appropriatedata entry interfaces that support semantically well defined and structured descriptivedata. The approach taken maps the ontology to a system domain model, which ataxonomist can then specialise using their domain expertise, for their data entry needs asrequired for individual projects. Based on this specialised domain knowledge, thesystem automatically generates appropriate data entry interfaces that capture dataconsistent with the original ontology. Compared with traditional model-based userautomatic interface development environments, this approach also has the potential toreduce the labour requirements for the expert developer.The approach has also been successfully tested to generate data entry interfaces basedon an XML schema for the exchange of biodiversity datasets

Interactive visualisation tools for supporting taxonomists working practice.

The necessity for scientists and others to use consistent terminology has recently beenregarded as fundamental to advancing scientific research, particularly where data fromdisparate sources must be shared, compared or integrated. One area where there aresignificant difficulties with the quality of collected data is the field of taxonomicdescription. Taxonomic description lies at the heart of the classification of organismsand communication of ideas of biodiversity. As part of their working practice,taxonomists need to gather descriptive data about a number of specimens on aconsistent basis for individual projects. Collecting semantically well-defined structureddata could improve the clarity and comparability of such data. No tools howevercurrently exist to allow taxonomists to do so within their working practice.Ontologies are increasingly used to describe and define complex domain data. As a partof related research an ontology of descriptive terminology for controlling the storageand use of flowering plant description data was developed.This work has applied and extended model-based user interface developmentenvironments to utilise such an ontology for the automatic generation of appropriatedata entry interfaces that support semantically well defined and structured descriptivedata. The approach taken maps the ontology to a system domain model, which ataxonomist can then specialise using their domain expertise, for their data entry needs asrequired for individual projects. Based on this specialised domain knowledge, thesystem automatically generates appropriate data entry interfaces that capture dataconsistent with the original ontology. Compared with traditional model-based userautomatic interface development environments, this approach also has the potential toreduce the labour requirements for the expert developer.The approach has also been successfully tested to generate data entry interfaces basedon an XML schema for the exchange of biodiversity datasets