  Modeling complex networks has been the focus of much research for over a
decade. Preferential attachment (PA) is considered a common explanation to the
self organization of evolving networks, suggesting that new nodes prefer to
attach to more popular nodes. The PA model results in broad degree
distributions, found in many networks, but cannot explain other common
properties such as: The growth of nodes arriving late and Clustering (community
structure). Here we show that when the tendency of networks to adhere to trends
is incorporated into the PA model, it can produce networks with such
properties. Namely, in trending networks, newly arriving nodes may become
central at random, forming new clusters. In particular, we show that when the
network is young it is more susceptible to trends, but even older networks may
have trendy new nodes that become central in their structure. Alternatively,
networks can be seen as composed of two parts: static, governed by a power law
degree distribution, and a dynamic part governed by trends, as we show on Wiki
pages. Our results also show that the arrival of trending new nodes not only
creates new clusters, but also has an effect on the relative importance and
centrality of all other nodes in the network. This can explain a variety of
real world networks in economics, social and online networks, and cultural
networks. Products popularity, formed by the network of people's opinions,
exhibit these properties. Some lines of products are increasingly susceptible
to trends and hence to shifts in popularity, while others are less trendy and
hence more stable. We believe that our findings have a big impact on our
understanding of real networks.
