  The preferential attachment (PA) process is a popular theory for explaining
network power-law degree distributions. In PA, the probability that a new
vertex adds an edge to an existing vertex depends on the connectivity of the
target vertex. In real-world networks, however, each vertex may have asymmetric
accessibility to information. Here we address this issue using a new
network-generation mechanism that incorporates asymmetric accessibility to
upstream and downstream information. We show that this asymmetric information
accessibility directly affects the power-law exponent, producing a broad range
of values that are consistent with observations. Our findings shed new light on
the possible mechanisms in three important real-world networks: a citation
network, a hyperlink network, and an online social network.
