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

A survey of frequent subgraph mining algorithms

AbstractGraph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining and proposes solutions to address the main research issues.</jats:p

Performance evaluation of keyword extraction methods and visualization for student online comments

Topic keyword extraction (as a typical task in information retrieval) refers to extracting the core keywords from document topics. In an online environment, students often post comments in subject forums. The automatic and accurate extraction of keywords from these comments are beneficial to lecturers (particular when it comes to repeatedly delivered subjects). In this paper, we compare the performance of traditional machine learning algorithms and two deep learning methods in extracting topic keywords from student comments posted in subject forums. For this purpose, we collected student comment data from a period of two years, manually tagging part of the raw data for our experiments. Based on this dataset, we comprehensively compared the five typical algorithms of naïve Bayes, logistic regression, support vector machine, convolutional neural networks, and Long Short-Term Memory with Attention (Att-LSTM). The performances were measured by the four evaluation metrics. We further examined the keywords by visualization. From the results of our experiment and visualization, we conclude that the Att-LSTM method is the best approach for topic keyword extraction from student comments. Further, the results from the algorithms and visualization are symmetry, to some degree. In particular, the extracted topics from the comments posted at the same stages of different teaching sessions are, almost, reflection symmetry

Tecnologías digitales disruptivas aplicadas a la gestión de la pandemia por COVID-19: un análisis a través de la producción científica

Se trata de un libro con una función divulgativa, que pretende recoger y analizar las distintas tecnologías que se han aplicado hasta ahora para la gestión de la pandemia mundial por COVID-19.Este libro presenta un estudio acerca de las tecnologías digitales disruptivas (Internet of Things, Machine Learning, Blockchain y otras) que se han aplicado a la gestión de la pandemia ocasionada por la COVID-19. La investigación se ha llevado a cabo a través de un análisis cienciométrico −basado en minería de textos− de la producción científica publicada al respecto a lo largo de un período de año y medio (2020 y mitad de 2021) y, a este respecto, se ha considerado Scopus como fuente de datos principal y Web of Science como secundaria (a efectos comparativos). De esta manera, por medio de la utilización del potente software VOSviewer, se ofrecen multitud de resultados −ilustrados por los correspondientes mapas bibliométricos− como la evolución temporal del número de publicaciones, la producción y el número de coautorías por países, los temas (topics) y autores más prolíficos o un ranking de los artículos más referenciados. En definitiva, en este libro, se pretende ofrecer una visión lo más completa y actualizada posible de cómo la inteligencia artificial y ciertas tecnologías digitales emergentes han contribuido, de manera esencial, a cuestiones de predicción, seguimiento, diagnóstico, tratamiento y prevención de la COVID-19

Clustering graphs for visualization via node similarities

Graph visualization is commonly used to visually model relations in many areas. Examples include Web sites, CASE tools, and knowledge representation. When the amount of information in these graphs becomes too large, users, however, cannot perceive all elements at the same time. A clustered graph can greatly reduce visual complexity by temporarily replacing a set of nodes in clusters with abstract nodes. This paper proposes a new approach to clustering graphs. The approach constructs the node similarity matrix of a graph that is derived from a novel metric of node similarity. The linkage pattern of the graph is thus encoded into the similarity matrix, and then one obtains the hierarchical abstraction of densely linked subgraphs by applying the k-means algorithm to the matrix. A heuristic method is developed to overcome the inherent drawbacks of the k-means algorithm. For clustered graphs we present a multilevel multi-window approach to hierarchically drawing them in different abstract level views with the purpose of improving their readability. The proposed approaches demonstrate good results in our experiments. As application examples, visualization of part of Java class diagrams and Web graphs are provided. We also conducted usability experiments on our algorithm and approach. The results have shown that the hierarchically clustered graph used in our system can improve user performance for certain types of tasks

Frequent subgraph mining algorithms on weighted graphs

This thesis describes research work undertaken in the field of graph-based knowledge discovery (or graph mining). The objective of the research is to investigate the benefits that the concept of weighted frequent subgraph mining can offer in the context of the graph model based classification. Weighted subgraphs are graphs where some of the vertexes/edges are considered to be more significant than others. How to discover frequent sub-structures with different strengths is the main issue to be resolved in this thesis. The main approach to addressing this issue is to integrate weight constraints into the frequent subgraph mining process. It is suggested that the utilization of weighted frequent subgraph mining generates more discriminate and significant subgraphs, which will have application in, for example, the classification and clustering of graph data

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions