Visualization of Bibliographic Networks with a Reshaped Landscape

We describe a novel approach to visualize bibliographic networks that facilitates the simultaneous identification of clusters (e.g., topic areas) and prominent entities (e.g., surveys or landmark papers). While employing the landscape metaphor proposed in several earlier works, we introduce new means to determine relevant parameters of the landscape. Moreover, we are able to compute prominent entities, clustering of entities, and the landscape’s surface in a surprisingly simple and uniform way. The effectiveness of our network visualizations is illustrated on

data from the graph drawing literature. Categories and Subject Descriptors (according to ACM CCS): H.3.3 [Information Search and Retrieval]: Informa- tion filtering 1. Introduction Bibliographic analysis24 uses publication data to structure and summarize a scientific field. These data are often given in the form of networks, with nodes representing authors, journals, or publications, and edges representing relations between these entities such as authorship, collaboration, or citation. We present an approach to analyze and visualize biblio- graphic networks using uniform algorithms to determine the prominent entities in the network, to spatially represent the clustering of the network, and to compute a surface for a landscape visualization of results. Since we propose an integrated method of analysis and visualization directed at particular aspects of bibliographic analysis, it may serve as a specialized component in more elaborate systems,10; 5; 9 and in particular as a communica- tion/exploration back-end for systems that specialize in ex- tracting and presenting network data.7; 23 This paper is organized as follows. In Sect. 2 we recall the definition of Kleinberg’s hubs & authorities indices15 and sketch their use in the analysis of bibliographic data. Based on similar principles, a new method for two-dimensional layout of bibliographic networks preserving the scientific topography is presented in Sect. 3. In Sect. 4, index and layout are turned into a landscape visualization, again us- ing the same algorithmic principles. An illustrative example comprised of publications in proceedings of Graph Drawing Symposia is given in Sect. 5. 2. Landmark Papers To identify prominent entities in bibliographic networks, we determine the structural importance of vertices according to their position in the graph. Many concepts formalizing this notion are in use, but the concept of hubs & authorities,15 though originally conceived to improve relevance ranking in Web search engines, appears to be particularly suitable for bibliographic networks. In this section, we present an alter- native derivation of these indices to emphasize the similarity of their computation with those in later sections. We assume familiarity with basic matrix properties and computations.12 A straightforward notion of prominence in undirected graphs, commonly applied in the analysis of social net- works,22 is the idea that the importance of a vertex is de- termined by the importance of its neighbors. According to the following definition, the importance assigned to a vertex is proportional to the total importance of its neighbors. Definition 1 (eigenvector centrality4) Let A be the adja- cency...

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