In this paper we propose two methods to derive two different kinds of node similarities in a network based on their neighborhood. The first similarity measure focuses on the overlap of direct and indirect neighbors. The second similarity compares nodes based on the structure of their - possibly also very distant - neighborhoods. Instead of using standard node measures, both similarities are derived from spreading activation patterns over time. Whereas in the first method the activation patterns are directly compared, in the second method the relative change of activation over time is compared. We apply both methods to a real-world graph dataset and discuss the results.

Distance aware tag clouds add visualization of relations between terms to standard tag clouds. In addition to term importance (which is usually depicted through font size) the placement of terms represents the relation between words in the corpus. These relations are modeled as similarities between words and are visualized via the distance between the corresponding tags in the tag cloud. In this paper a modified multi-dimensional scaling (MDS) approach for tag positioning is presented. Applying standard MDS results in unsatisfying and unusable representations due to two problems. The first problem stems from word overlap which is orthogonal to the second problem: excessive empty space. Both of these shortcomings are addressed by introducing methods for overlap removal and empty space reduction. We show that these two modifications only moderately increase the resulting MDS stress value of the new positioning while they remove most of the overlaps and reduce the amount of white space.

Almost every application of spreading activation is accompanied by its own set of often heuristic restrictions on the dynamics. We show that in constraint-free scenarios spreading activation would actually yield query-independent results, so that the specific choice of restrictions is not only a pragmatic computational issue, but crucially determines the outcome.

This paper presents two approaches to visually analyze the topic shift of a pool of documents over a given period of time. The first of the proposed methods is based on a multi-dimensional scaling algorithm, which places vectors representing terms occurring in certain years (period-frequency-vectors) in a spatial, two-dimensional space. This kind of visualization enables the detection of terms occurring in documents, published in particular years, or terms spread over different years. The second method uses a graph based approach. Publishing dates of documents, as well as their terms are represented by the vertices of a graph. Terms related to a specific publishing year are connected to the vertex of the year via an edge. By usage of activation spreading techniques, terms frequently occurring in documents published in particular years can be discovered visually. We tested both approaches with 2431 abstracts of papers published in the IEEE Transactions on SMC-A, SMC-B, and SMC-C in the years 1996 to 2006. Our experiments indicate that a number of interesting terms can be nicely separated in clumps according to individual years or periods of time. In addition, one can visualize the emergence of specific terms over certain periods of time and how these and other terms fade away again later.

This paper presents an approach to visualizing and exploring high-dimensional rules in two-dimensional views. The proposed method uses multi-dimensional scaling to place the rule centers and subsequently extends the rules' regions to depict their overlap. This results not only in a visualization of the rules' distribution but also enables the relationship to their immediate neighbors to be judged. The proposed technique is illustrated and discussed on a number of well-known benchmark data sets.