Co-Citation Count vs Correlation for Influence Network Visualization

Abstract

Visualization of author or document influence networks as a two-dimensional image can provide key insights into the direct influence of authors or documents on each other in a document collection. The influence network is constructed based on the minimum spanning tree, in which the nodes are documents and an edge is the most direct influence between two documents. Influence network visualizations have typically relied on co-citation correlation as a measure of document similarity. That is, the similarity between two documents is computed by correlating the sets of citations to each of the two documents. In a different line of research, co-citation count (the number of times two documents are jointly cited) has been applied as a document similarity measure. In this work, we demonstrate the impact of each of these similarity measures on the document influence network. We provide examples, and analyze the significance of the choice of similarity measure. We show that correlation-based visualizations exhibit chaining effects (low average vertex degree), a manifestation of multiple minor variations in document similarities. These minor similarity variations are absent in count-based visualizations. The result is that count-based influence network visualizations are more consistent with the intuitive expectation of authoritative documents being hubs that directly influence large numbers of documents.