  Modern experiments in many disciplines generate large quantities of network
(graph) data. Researchers require aesthetic layouts of these networks that
clearly convey the domain knowledge and meaning. However, the problem remains
challenging due to multiple conflicting aesthetic criteria and complex
domain-specific constraints. In this paper, we present a strategy for
generating visualizations that can help network biologists understand the
protein interactions that underlie processes that take place in the cell.
Specifically, we have developed Flud, an online game with a purpose (GWAP) that
allows humans with no expertise to design biologically meaningful graph layouts
with the help of algorithmically generated suggestions. Further, we propose a
novel hybrid approach for graph layout wherein crowdworkers and a simulated
annealing algorithm build on each other's progress. To showcase the
effectiveness of Flud, we recruited crowd workers on Amazon Mechanical Turk to
lay out complex networks that represent signaling pathways. Our results show
that the proposed hybrid approach outperforms state-of-the-art techniques for
graphs with a large number of feedback loops. We also found that the
algorithmically generated suggestions guided the players when they are stuck
and helped them improve their score. Finally, we discuss broader implications
for mixed-initiative interactions in human computation games.
