  Force-directed (FD) algorithms can be used to explore relationships in social
networks, visualize money markets, and analyze transaction networks. However,
FD algorithms are mainly designed for visualizing static graphs in which the
topology of the networks remains constant throughout the calculation. In
contrast to static graphs, nodes and edges in dynamic graphs can be added or
removed as time progresses. In these situations, existing FD algorithms do not
scale well, since any changes in the topology will trigger these algorithms to
completely restart the entire computation. To alleviate this problem, we
propose a design and implementation of five online FD algorithms to visualize
dynamic graphs while maintaining their native force models. The online FD
algorithms developed in this paper are able to reuse the force models of
existing FD algorithms without significant modifications. To evaluate the
effectiveness of the proposed approach, online FD algorithms are compared
against static FD algorithms for visualizing dynamic graphs. Experimental
results show that among the five algorithms evaluated, the online FD algorithm
achieves the best number of edge crossings and the standard deviation of edge
lengths for visualizing dynamic graphs.
