Stephen C. North

Topological fisheye views for visualizing large graphs

Graph drawing is a basic visualization tool that works well for graphs having up to hundreds of nodes and edges. At greater scale, data density and occlusion problems often negate its effectiveness. Conventional pan-and-zoom, multiscale, and geometric fisheye views are not fully satisfactory solutions to this problem. As an alternative, we propose a topological zooming method. It precomputes a hierarchy of coarsened graphs that are combined on-the-fly into renderings, with the level of detail dependent on distance from one or more foci. A related geometric distortion method yields constant information density displays from these renderings.

CartoDraw: a fast algorithm for generating contiguous cartograms

Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. We formally define a family of cartogram drawing problems. We show that even simple variants are unsolvable in the general case. Because the feasible variants are NP-complete, heuristics are needed to solve the problem. Previously proposed solutions suffer from problems with the quality of the generated drawings. For a cartogram to be recognizable, it is important to preserve the global shape or outline of the input map, a requirement that has been overlooked in the past. To address this, our objective function for cartogram drawing includes both global and local shape preservation. To measure the degree of shape preservation, we propose a shape similarity function, which is based on a Fourier transformation of the polygons curvatures. Also, our application is visualization of dynamic data, for which we need an algorithm that recalculates a cartogram in a few seconds. None of the previous algorithms provides adequate performance with an acceptable level of quality for this application. We therefore propose an efficient iterative scanline algorithm to reposition edges while preserving local and global shapes. Scanlines may be generated automatically or entered interactively to guide the optimization process more closely. We apply our algorithm to several example data sets and provide a detailed comparison of the two variants of our algorithm and previous approaches.

Visualization research with large displays [analysis of communication networks and services]

We describe our research at AT&T Infolab on using large displays to interactively analyze and visualize AT&Ts communication networks and services.

Visual support for analyzing network traffic and intrusion detection events using TreeMap and graph representations

Network security depends heavily on automated Intrusion Detection Systems (IDS) to sense malicious activity. Unfortunately, IDS often deliver both too much raw information, and an incomplete local picture, impeding accurate assessment of emerging threats. We propose a system to support analysis of IDS logs, that visually pivots large sets of Net-Flows. In particular, two visual representations of the flow data are compared: a TreeMap visualization of local network hosts, which are linked through hierarchical edge bundles with the external hosts, and a graph representation using a force-directed layout to visualize the structure of the host communication patterns. Three case studies demonstrate the capabilities of our tool to 1) analyze service usage in a managed network, 2) detect a distributed attack, and 3) investigate hosts in our network that communicate with suspect external IPs.

Pixel based visual data mining of geo-spatial data

Abstract In many application domains, data is collected and referenced by its geo-spatial location. Spatial data mining, or the discovery of interesting patterns in such databases, is an important capability in the development of database systems. A noteworthy trend is the increasing size of data sets in common use, such as records of business transactions, environmental data and census demographics. These data sets often contain millions of records, or even far more. This situation creates new challenges in coping with scale. For data mining of large data sets to be effective, it is also important to include humans in the data exploration process and combine their flexibility, creativity, and general knowledge with the enormous storage capacity and computational power of todays computers. Visual data mining applies human visual perception to the exploration of large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the formation and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper we give a short overview of visual data mining techniques, especially for analyzing geo-spatial data. We provide examples for effective visualizations of geo-spatial data in important application areas such as consumer analysis and census demographics.

Closing-the-loop in NVisionIP: integrating discovery and search in security visualizations

The field of security visualization is in need of a paradigm shift in order to allow visualization tools to be practically used by security engineers. Security engineers must complete two different tasks, that of discovery of a pattern, and that of searching for a pattern in a data set. Current security visualizations do not aid the user in creating symbolic rules that represent visual patterns. Transforming visual patterns to symbolic rules requires effort by the security engineer and detracts from their main task of discovering interesting patterns. In this paper we describe the idea of closing-the-loop, a system where symbolic rules are created from visual patterns.

Medial-axis-based cartograms

Cartographers and geographers were making cartograms for centuries before digital computers and displays became available. In data visualization, an area cartogram distorts a map by resizing its regions according to some external geography-related parameter, such as population or epidemiological data. Cartograms are difficult to draw by hand because its difficult to simultaneously optimize shape and area error while preserving the original maps topology. Automated methods for drawing cartograms have therefore received considerable interest. A method for generating cartograms that combines iterative relocation of a maps vertices with medial-axis-based transformations retains the input maps topology.

Visualizing geographic information: VisualPoints vs CartoDraw

Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. In this paper, we deal with the problem of making continuous cartograms that strictly retain the topology of the input mesh. We compare two algorithms that solve the continuous cartogram problem. The first one uses an iterative relocation of vertices based on scanlines. This algorithm explicitly accounts for induced shape error. The second one is based on the Gridfit technique, which uses pixel-based distortion based on a quadtree-like data structure. The basic idea is to insert pixels, the number of which corresponds to a statistical parameter, into the data structure and distort the pixels such that every pixel obtains a unique, nonoverlapping position. Relocation of vertices of the map are positioned using the same distortion. We discuss the results obtained from both methods, compare their shape and area trade-offs as well as their efficiency, and show results from different applications.

Efficient cartogram generation: a comparison

Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. We deal with the problem of making continuous cartograms that strictly retain the topology of the input mesh. We compare two algorithms to solve the continuous cartogram problem. The first one uses an iterative relocation of the vertices based on scanlines. The second one is based on the Gridfit technique, which uses pixel-based distortion based on a quadtree-like data structure.

Interactive Visualization of Streaming Text Data with Dynamic Maps

The many endless rivers of text now available present a serious challenge in the task of gleaning, analyzing and discovering useful information. In this paper, we describe a methodology for visualizing a text stream in real-time, modeled as a dynamic graph and its derived map. The approach automatically groups similar messages into clusters displayed as “countries”, with keyword summaries, using semantic analysis, graph clustering and map generation techniques. It handles the need for visual stability across time by dynamic graph layout and Procrustes projection, enhanced with a novel stable component packing algorithm. The result provides a continuous, succinct view of ever-changing topics of interest. To make these ideas concrete, we describe their application to an experimental web service called TwitterScope.