The rate at which nodes in evolving social networks acquire links (friends,
citations) shows complex temporal dynamics. Preferential attachment and link
copying models, while enabling elegant analysis, only capture rich-gets-richer
effects, not aging and decline. Recent aging models are complex and heavily
parameterized; most involve estimating 1-3 parameters per node. These
parameters are intrinsic: they explain decline in terms of events in the past
of the same node, and do not explain, using the network, where the linking
attention might go instead. We argue that traditional characterization of
linking dynamics are insufficient to judge the faithfulness of models. We
propose a new temporal sketch of an evolving graph, and introduce several new
characterizations of a network's temporal dynamics. Then we propose a new
family of frugal aging models with no per-node parameters and only two global
parameters. Our model is based on a surprising inversion or undoing of triangle
completion, where an old node relays a citation to a younger follower in its
immediate vicinity. Despite very few parameters, the new family of models shows
remarkably better fit with real data. Before concluding, we analyze temporal
signatures for various research communities yielding further insights into
their comparative dynamics. To facilitate reproducible research, we shall soon
make all the codes and the processed dataset available in the public domain