Community detection aims at discovering the structure, behavior, dynamics, and
organization of a complex network by finding cohesive groups where nodes (entities) are, in …

The emergence of online social networks has revolutionized millions of web users' behavior
so that their interactions with each other produce huge amounts of data on different …

In social network analysis, a key issue is the detection of meaningful communities. This
problem consists of finding groups of people who are both connected and semantically …

Complex networks contain various interactions among similar or different entities. These
kinds of networks are called multi-relational networks, in which each layer corresponds to a …

Learners' community choice has a crucial role in e‐learning effectiveness. Indeed, individual
and structural factors (ie, learners pre‐existing profiles and networks of interactions) …

Cancer is one of the most dangerous threats to human health. Accurate identification of
anticancer peptides (ACPs) is valuable for the development and design of new anticancer …

The real world can be characterized as a complex network sto in symmetric matrix.
Community discovery (or community detection) can effectively reveal the common features …

The semantic social network is a kind of network that contains enormous nodes and
complex semantic information, and the traditional community detection algorithms could not …

Multidimensional community discovering in heterogeneous social networks is an important
issue. Many approaches have been proposed for community discovering in heterogeneous …

Articulateness and plasticity are two essential attributes that make a graph as an efficient
model to real life problems. Nowadays, the attributed graph is received lots of attentions …