SPECTRUM-BASED AND COLLABORATIVE NETWORK TOPOLOGY ANALYSIS AND VISUALIZATION

Networks are of significant importance in many application domains, such as World Wide Web and social networks, which often embed rich topological information. Since network topology captures the organization of network nodes and links, studying net- work topology is very important to network analysis. In this dissertation, we study networks by analyzing their topology structure to explore community structure, the relationship among network members and links as well as their importance to the belonged communities. We provide new network visualization methods by studying network topology through two aspects: spectrum-based and collaborative visualiza- tion techniques. For the spectrum-based network visualization, we use eigenvalues and eigenvectors to express network topological features instead of using network datasets directly. We provide a visual analytics approach to analyze unsigned networks based on re- cent achievements on spectrum-based analysis techniques which utilize the features of node distribution and coordinates in the high dimensional spectral space. To assist the interactive exploration of network topologies, we have designed network visual- ization and interactive analysis methods allowing users to explore the global topology structure. Further, to address the question of real-life applications involving of both positive and negative relationships, we present a spectral analysis framework to study both signed and unsigned networks. Our framework concentrates on two problems of net- work analysis - what are the important spectral patterns and how to use them to study signed networks. Based on the framework, we present visual analysis methods, which guide the selection of k-dimensional spectral space and interactive exploration of network topology. With the increasing complexity and volume of dynamic networks, it is important to adopt strategies of joint decision-making through developing collaborative visualiza- tion approaches. Thus, we design and develop a collaborative detection mechanism with matrix visualization for complex intrusion detection applications. We establish a set of collaboration guidelines for team coordination with distributed visualization tools. We apply them to generate a prototype system with interactions that facilitates collaborative visual analysis. In order to evaluate the collaborative detection mechanism, a formal user study is presented. The user study monitored participants to collaborate under co-located and distributed collaboration environments to tackle the problems of intrusion detection. We have observed participants’ behaviors and collected their performances from the aspects of coordination and communication. Based on the results, we conclude several coordination strategies and summarize the values of communication for collaborative visualization. Our visualization methods have been demonstrated to be efficient topology explo- ration with both synthetic and real-life datasets in spectrum-based and collaborative exploration. We believe that our methods can provide useful information for future design and development of network topology visualization system

Techniques for Interactive Graph Drawing

There are two complementary approaches to graph drawing: algorithmic and interactive. By algorithmic graph drawing we mean the automatic placement of vertices and routing of edges using heuristics to improve understandability. Interactive graph drawing, on the other hand, implies that a user directly influences at least some low-level aspects of graph layout, either by direct placement of vertices and edges, or by providing some form of feedback to the system. Interactively engaging the user is in some instances unavoidable. When a user desires a difficult-to-quantify aesthetic quality of the graph, or is incrementally constructing a graph (such as a state diagram for an automaton) as part of a problem-solving task or pedagogical exercise, it is difficult or impossible to employ algorithmic graph drawing. In these instances it is more natural to employ a sketching metaphor in which a user draws the graph in a traditional sense. A common strategy for making interactive drawing of diagrams immediately understandable is to incorporate physical analogies; that is, to mimic some familiar real-world appearance or process. For example, the hand-drawn appearance of a diagram can be retained by preserving its unevenness and through the use of textures that impart the appearance of physical writing implements, such as pencil, ink, or chalk. It can be advantageous to retain this type of appearance in situations where user-drawn and computer-drawn diagrams coexist. The behavior of a graph that is interactively altered can also incorporate useful physical metaphors. For example, the edges adjacent to a vertex that is interactively re-positioned by a user can mimic physical linkages that stretch when pulled, and bend or contract when pushed. Such behaviors are natural in that they are almost immediately plausible and predictable to a user. However, not all interactive techniques can benefit from physical metaphors, for some transcend the capabilities of physical media. For example, recognition of hand-drawn elements, such as those representing vertices and edges, are often combined with gradual morphing that can improve the appearance of the diagram. Since physical writing surfaces exhibit no analogous behavior, a spectrum of possibilities has been explored. One approach that has been successfully applied in the context of graph drawing is the approach known as fluid sketching in which drawing and morphing occur nearly simultaneously. There are many potential combinations of algorithmic and interactive graph drawing to explore, such as the graceful re-routing of edges as a user interactively re-positions a vertex, or systems in which a user modifies automatically generated graphs, or in which local improvements are automatically applied to user-drawn graphs