Networks (or graphs) appear as dominant structures in diverse domains, including
sociology, biology, neuroscience and computer science. In most of the aforementioned …

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 …

A spectral algorithm for community detection is presented. The algorithm consists of three
stages:(1) matrix factorization of two matrix forms, square signless Laplacian for unipartite …

In directed graphs, relationships are asymmetric and these asymmetries contain essential
structural information about the graph. Directed relationships lead to a new type of clustering …

In this paper, we develop the idea to partition the edges of a weighted graph in order to
uncover overlapping communities of its nodes. Our approach is based on the construction of …

Community detection in networks is commonly performed using information about
interactions between nodes. Recent advances have been made to incorporate multiple …

Graph embedding methods are becoming increasingly popular in the machine learning
community, where they are widely used for tasks such as node classification and link …

Complex network topologies present interesting and surprising properties, such as
community structures, which can be exploited to optimize communication, to find new …

Network science plays a central role in understanding and modeling complex systems in
many areas including physics, sociology, biology, computer science, economics, politics …

Uncovering the community structure exhibited by real networks is a crucial step toward an
understanding of complex systems that goes beyond the local organization of their …