  Network models with preferential attachment, where new nodes are injected
into the network and form links with existing nodes proportional to their
current connectivity, have been well studied for some time. Extensions have
been introduced where nodes attach proportionally to arbitrary fitness
functions. However, in these models, attaching to a node always increases the
ability of that node to gain more links in the future. We study network growth
where nodes attach proportionally to the clustering coefficients, or local
densities of triangles, of existing nodes. Attaching to a node typically lowers
its clustering coefficient, in contrast to preferential attachment or
rich-get-richer models. This simple modification naturally leads to a variety
of rich phenomena, including aging, non-Poissonian bursty dynamics, and
community formation. This theoretical model shows that complex network
structure can be generated without artificially imposing multiple dynamical
mechanisms and may reveal potentially overlooked mechanisms present in complex
systems.
