Network evolution is a ubiquitous phenomenon in a wide variety of complex systems. There is an increasing interest in statistically modeling the evolution of complex networks such as small-world networks and scale-free networks. In this article, we address a practical issue concerning the visualization of network evolution. We compare the visualizations of co-citation networks of scientific publications derived by two widely known link reduction algorithms, namely minimum spanning trees (MSTs) and Pathfinder networks (PFNETs). Our primarily goal is to identify the strengths and weaknesses of the two methods in fulfilling the need for visualizing evolving networks. Two criteria are derived for assessing visualizations of evolving networks in terms of topological properties and dynamical properties. We examine the animated visualization models of the evolution of botulinum toxin research in terms of its co-citation structure across a 58-year span (1945-2002). The results suggest that although high-degree nodes dominate the structure of MST models, such structures can be inadequate in depicting the essence of how the network evolves because MST removes potentially significant links from high-order shortest paths. In contrast, PFNET models clearly demonstrate their superiority in maintaining the cohesiveness of some of the most pivotal paths, which in turn make the growth animation more predictable and interpretable. We suggest that the design of visualization and modeling tools for network evolution should take the cohesiveness of critical paths into account.

What are the hottest computer science research topics today? Which research areas are experiencing steady decline? How many co-authors are typical for a research paper today and 20 years ago? Who are the most prolific writers? In this paper, we attempt to address these questions as well as study collaboration patterns, research communities, interactions between related research specialties, and the evolution of these characteristics through time. For our analysis we use data from the Association of Computing Machinery's Digital Library of Scientific Literature (ACM Portal) which contains over a hundred thousand research papers and authors. We use a novel technique for visualization of large graphs that evolve through time. Given a dynamic graph, the layout algorithm produces two-dimensional representations of each time-slice, while preserving the mental map of the graph from one slice to the next. A combined view, with all the time-slices can also be viewed and explored. Graphs with tens of thousands of vertices and edges, resulting from specific queries to our local copy of the ACM database, are generated and displayed in seconds. The images in this paper are produced by a graph layout tool which uses the dynamic graph layout algorithm.

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Author co-citation analysis (ACA) has been widely used in bibliometrics as an analytical method in analyzing the intellectual structure of science studies. It can be used to identify authors fromthe same or similar research fields. However, such analysis method relies heavily on statistical tools to perform the analysis and requires human interpretation. Web Citation Database is a data warehouse used for storing citation indices of Web publications. In this paper, we propose a mining process to automate the ACA based on the Web Citation Database. The mining process uses agglomerative hierarchical clustering (AHC) as the mining technique for author clustering and multidimensional scaling (MDS) for displaying author cluster maps. The clustering results and author cluster map have been incorporated into a citation-based

Information Visualization is concerned with the art and technology of designing and implementing highly interactive, computer supported tools for knowledge discovery in large non-spatial databases. Information Visualization displays, also known as information spaces or graphic spatializations, differ from ordinary data visualization and geovisualization in that they may be explored as if they represented spatial information. Information spaces are very often based on spatial metaphors such as location, distance, region, scale, etc., thus potentially affording spatial analysis techniques and geovisualization approaches for data exploration and knowledge discovery. Two major concerns in spatialization can be identified from a GIScience/ geovisualization perspective: the use of space as a data generalization strategy, and the use of spatial representations or maps to depict these data abstractions. A range of theoretical and technical research questions needs to be addressed to assure the construction of cognitively adequate spatializations. In the first part of this chapter we propose a framework for the construction of cognitively plausible semantic information spaces. This theoretical scaffold is based on geographic information theory and includes principles of ontological modeling such as semantic generalization (spatial primitives), geometric generalization (visual variables), association (source–target domain mapping through spatial metaphors), and aggregation (hierarchical organization). In the remainder of the chapter we discuss ways in which the framework may be applied towards the design of cognitively adequate spatializations.

Dynamic criminal network analysis is important for national security but also very challenging. However, little research has been done in this area. In this paper we propose to use several descriptive measures from social network analysis research to help detect and describe changes in criminal organizations. These measures include centrality for individuals, and density, cohesion, and stability for groups. We also employ visualization and animation methods to present the evolution process of criminal networks. We conducted a field study with several domain experts to validate our findings from the analysis of the dynamics of a narcotics network. The feedback from our domain experts showed that our approaches and the prototype system could be very helpful for capturing the dynamics of criminal organizations and assisting crime investigation and criminal prosecution. 1.

Tracking the growth of scientific knowledge has become increasingly challenging even in one's own specialized field due to the vast amount of new scientific publications become available. As a rapidly advancing and expanding field of computing and information technology, information visualization has focused on the discovery of interrelationships among various scientific publications. However, visualizing intrinsic structures among documents in scientific literatures can only capture some aspects of scientific knowledge. For example, the number of citations received by a scientific work is a widely accepted hallmark of its significance. This chapter describes approaches to the visualization of knowledge structures with emphasis on the role of citation-based methods. Instead of relying upon occurrence patterns of content-bearing words, visualization of knowledge structures aims to capture perceived intellectual structures of a particular knowledge domain. An ultimate goal for the visualization of knowledge structures is to provide scientists with a tool that can detect the existence of a scientific paradigm and movements of such paradigms. This chapter also includes a summary of the history of tracking the growth of scientific knowledge. The state of the art is presented to highlight the trend of future research.

The move towards integrated international Digital Libraries o#ers the opportunity of creating comprehensive data on citation networks.

We propose WebRelievo, a system for visualizing and analyzing the evolution of the web structure based on a large Web archive with a series of snapshots. It visualizes the evolution with a time series of graphs, in which nodes are web pages, and edges are relationships between pages. Graphs can be clustered to show the overview of changes in graphs. WebRelievo aligns these graphs according to their time, and automatically determines their layout keeping positions of nodes synchronized over time, so that the user can keep track pages and clusters. This visualization enables us to understand when pages appeared, how their relationships have evolved, and how clusters are merged and split over time. Current implementation of WebRelievo is based on six Japanese web archives crawled from 1999 to 2003. The user can interactively browse those graphs by changing the focused page and by changing layouts of graphs. Using WebRelievo we can answer historical questions, and to investigate changes in trends on the Web. We show the feasibility of WebRelievo by applying it to tracking trends in P2P systems and search engines for mobile phones, and to investigating link spamming.

The dynamic social network visualizer “DySoN ” (Dynamic Social Networks) aims at understanding patterns and structural changes in dynamic social networks that evolve over time via an interactive visualization approach. As an alternative and supplementation to the numerous other approaches to visualization of social network data and as an attempt to overcome some of the drawbacks of these approaches, DySoN interactively visualizes streaming event data of social interactions by an interactive three-dimensional model of interpolated NURBS ”tubes”, representing activity and social proximity within a given set of actors during a given time period by using three dimensions of temporal information mapping: spatial density (tube distance), tubecolor and tube-diameter. We use a self assembled large collaboration network of Jazz musicians with a straightforward semantics for the computation of relation strengths for the evaluation of the approach. We also discuss applications of the concept for awareness services in mobile peer to peer social networks, which exhibit a vivid measurable social micro dynamics in time and space.