We demonstrate a system that supports the visual explo- ration of collaboration networks. The system leverages the notion of fractional cores introduced in earlier work to rank vertices in a collaboration network and filter vertices' neigh- borhoods. Fractional cores build on the idea of graph degen- eracy as captured by the notion of k-cores in graph theory and extend it to undirected edge-weighted graphs. In a co- authorship network, for instance, the fractional core index of an author intuitively reflects the degree of collaboration with equally or higher-ranked authors. Our system has been deployed on a real-world co-authorship network derived from DBLP, demonstrating that the idea of fractional cores can be applied even to large-scale networks. The system provides an easy-to-use interface to query for the fractional core index of an author, to see who the closest equally or higher-ranked co-authors are, and explore the entire co-authorship network in an incremental manner.