Abstract
Personal networks formed within scientific communities and the collaborations they yield are one of the driving forces behind innovation and new discoveries. Luckily, successful collaboration produces analyzable data points in the form of publications that allow us to learn and understand some of the connections and collaborative structures in a scientific community. Co-author information is one important aspect of this, and various solutions to the fundamental visualization problems of co-author graphs exist. In this work, we introduce ColTop, a multi-level, interactive graph visualization system that allows users to effectively analyze publication data. It combines coauthor information with other meta-data and information extracted from textual content to support comprehensive analyses. ColTop includes a novel, heuristics-based approach to create a meaningful abstraction of co-author networks, and enriches them with topic information. To demonstrate the applicability of our approach, we discuss an example analysis scenario based on a practical data set.