The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is …

The modularity of a network quantifies the extent, relative to a null model network, to which
vertices cluster into community groups. We define a null model appropriate for bipartite …

Modular structure is ubiquitous in real-world complex networks, and its detection is important
because it gives insights into the structure–functionality relationship. The standard approach …

A multiobjective genetic algorithm to uncover community structure in complex network is
proposed. The algorithm optimizes two objective functions able to identify densely …

Graph vertices are often organized into groups that seem to live fairly independently of the
rest of the graph, with which they share but a few edges, whereas the relationships between …

The ubiquity of modular structure in real-world complex networks is the focus of attention in
many trials to understand the interplay between network topology and functionality. The best …

We present a reformulation of modularity that allows the analysis of the community structure
in networks of correlated data. The modularity preserves the probabilistic semantics of the …

Discovering evolving communities in dynamic networks is essential to important applications
such as analysis for dynamic web content and disease progression. Evolutionary clustering …

社区结构是许多真实复杂网络所普遍具有的一个结构特征, 它的存在有着广泛而重要的实际意义
. 近年来, 社区结构研究在生物学, 社会学和计算机科学等多个领域取得了丰硕成果 …

Modularity is a widely used quality measure for graph clusterings. Its exact maximization is
NP-hard and prohibitively expensive for large graphs. Popular heuristics first perform a …