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

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

05191 Abstracts Collection -- Graph Drawing

From 08.05.05 to 13.05.05, the Dagstuhl Seminar 05191 ``Graph Drawing\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

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

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

Interactive Visualization of the Market Graph

Financial markets are a fruitful area for data exploration, but the overwhelming size and dimension of the datasets usually prohibit meaningful analysis, especially on a large scale. Thus, there is a need for effective visualization tools to assist in efficiently exploring the data space. In this paper, we present a novel visualization tool that empowers a user with an interactive tool for finding meaningful relationships in historical or real-time financial market data. To reduce the size of data to be visualized, we summarize the areas of interest within the market graph by displaying only the pre-computed clusters, and aggregated inter- and intra-cluster edges. Target graph structures and their associated attributes are encoded using several visually intuitive schemes, and a modified force-directed model is used to layout the graph with minimal visual clutter while retaining important spatial properties. We also provide a brief overview of an underlying parallel data-mining pipeline which enables us to apply this visualization tool to real-time stock market data