Topology and weights are closely related in weighted complex networks and
this is reflected in their modular structure. We present a simple network model
where the weights are generated dynamically and they shape the developing
topology. By tuning a model parameter governing the importance of weights, the
resulting networks undergo a gradual structural transition from a module free
topology to one with communities. The model also reproduces many features of
large social networks, including the "weak links" property.

本文介绍了一个简单的网络模型，其中权重是动态生成的，并且塑造了演化的拓扑结构，通过调整控制权重重要性的模型参数，网络逐渐从无模块的拓扑结构转变为拥有社区的结构，该模型还能够重现许多大型社交网络的特征，包括 “弱联系” 属性。

带权网络中社群的出现

Emergence of communities in weighted networks

The recent high level of interest in weighted complex networks gives rise to
a need to develop new measures and to generalize existing ones to take the
weights of links into account. Here we focus on various generalizations of the
clustering coefficient, which is one of the central characteristics in the
complex network theory. We present a comparative study of the several
suggestions introduced in the literature, and point out their advantages and
limitations. The concepts are illustrated by simple examples as well as by
empirical data of the world trade and weighted coauthorship networks.

本文主要针对加权复杂网络中聚类系数的各种泛化方法进行了比较研究，并结合世界贸易和共同作者关系网络的实证数据阐述了其优缺点。