Real‐world networks usually have community structure, that is, nodes are grouped into
densely connected communities. Community detection is one of the most popular and best …

Local community detection aims to find a set of densely-connected nodes containing given
query nodes. Most existing local community detection methods are designed for a single …

The multiple kernel k-means (MKKM) and its variants utilize complementary information from
different kernels, achieving better performance than kernel k-means (KKM). However, the …

Given a graph, densest subgraph search reports a single subgraph that maximizes the
density (ie, average degree). To diversify the search results without imposing rigid …

Kernel-means clustering is a powerful tool for unsupervised learning of non-linearly
separable data. Its merits are thoroughly validated on a suite of simulated datasets and real …

Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to
hospitalization and death in individuals from high risk groups. Following infection, IAV …

Community detection is a flourishing research field with a plethora of applications ranging
from biology to sociology. Local community detection has emerged as a promising subfield …

Community detection is an important information mining task to uncover modular structures
in large networks. For increasingly common large network datasets, global community …

Over the years, detecting stable communities in a complex network has been a major
challenge in network science. The global and local structures help to detect communities …

Community structure is a typical feature of complex networks in cyberspace, and community
detection is considered to be crucial to understanding the topology structure, network …