It is nearly 20 years since the concept of a small-world network was first
quantitatively defined, by a combination of high clustering and short path
length; and about 10 years since this metric of complex network topology began
to be widely applied to analysis of neuroimaging and other neuroscience data as
part of the rapid growth of the new field of connectomics. Here we review
briefly the foundational concepts of graph theoretical estimation and
generation of small-world networks. We take stock of some of the key
developments in the field in the past decade and we consider in some detail the
implications of recent studies using high-resolution tract-tracing methods to
map the anatomical networks of the macaque and the mouse. In doing so, we draw
attention to the important methodological distinction between topological
analysis of binary or unweighted graphs, which have provided a popular but
simple approach to brain network analysis in the past, and the topology of
weighted graphs, which retain more biologically relevant information and are
more appropriate to the increasingly sophisticated data on brain connectivity
emerging from contemporary tract-tracing and other imaging studies. We conclude
by highlighting some possible future trends in the further development of
weighted small-worldness as part of a deeper and broader understanding of the
topology and the functional value of the strong and weak links between areas of
mammalian cortex.

该论文回顾了过去十年来小世界网络和权重网络分析在神经连接学领域的发展，并指出权重图的拓扑分析对于更复杂的脑连接数据更为重要，结合实例探讨了人类和小鼠的解剖神经网络拓扑结构，并探讨了未来对加权小世界拓扑结构更深层次、更广泛理解的趋势。